An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variat...An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.展开更多
Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semicon...Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.展开更多
The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific re...The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific research institutions. An integrated ANFIS model is built and the effectiveness of the model is verified by means of investigation data and their processing results. The model merges the learning mechanism of neural network and the language inference ability of fuzzy system, and thereby remedies the defects of neural network and fuzzy logic system. Result of this case study shows that the model is suitable for complicated socio-technical systems and has bright application perspective to solve such problems of prediction, evaluation and policy-making in managerial fields.展开更多
The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt...The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.展开更多
An adaptive neuro fuzzy inference system was used for classifying water quality status of river.It applied several physical and inorganic chemical indicators including dissolved oxygen,chemical oxygen demand,and ammon...An adaptive neuro fuzzy inference system was used for classifying water quality status of river.It applied several physical and inorganic chemical indicators including dissolved oxygen,chemical oxygen demand,and ammonia-nitrogen.A data set(nine weeks,total 845 observations)was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model.Up to 89.59%of the data could be correctly classified using this model.Such performance was more competitive when compared with artificial neural networks.It is applicable in evaluation and classification of water quality status.展开更多
An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demons...An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demonstrated.Extensive field experimental work was carried out in order to gather enough data for training and prediction.The statistical methods,such as the correlation coefficient,absolute fraction of variance and root mean square error,were given to compare the predicted and actual values for model validation.The simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite accurately.Therefore,the ANFIS approach can reliably be used for forecasting the performance of RUCT.展开更多
Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate.The purpose of this study was to evaluate the validity and reliability of an automatic post-processing met...Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate.The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures(contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.展开更多
Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method（NATM） is adopted.For this purpose,a systematic convergence measurement...Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method（NATM） is adopted.For this purpose,a systematic convergence measurement is usually implemented to adjust the design during the whole construction,and consequently deadly hazards can be prevented.In this study,a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system（ANFIS） approach.The proposed model used more than 1 000 datasets collected from two different tunnels,i.e.Daguan tunnel No.2 and Yaojia tunnel No.1,which are part of a tunnel located in Hunan Province,China.Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method.The data obtained from Daguan tunnel No.2 were used for model training,while the data from Yaojia tunnel No.1 were employed to evaluate the performance of the model.The input parameters include surrounding rock masses（SRM） rating index,ground engineering conditions（GEC） rating index,tunnel overburden（H）,rock density（？）,distance between monitoring station and working face（D）,and elapsed time（T）.The model’s performance was assessed by the variance account for（VAF）,root mean square error（RMSE）,mean absolute percentage error（MAPE） as well as the coefficient of determination（R2） between measured and predicted data as recommended by many researchers.The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity.展开更多
The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant...The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.展开更多
Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live l...Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel;design variables are the width and effective depth of the continuous beam and steel ratios for positive and negative moments. The constraints are built based on the ACI Building Code by considering the strength requirements of shear and the maximum positive and negative moments, the development length of flexural reinforcement, and the serviceability requirement of deflection. The objective function is to minimize the total cost of steel and concrete. The optimal data found from the genetic algorithm are divided into three groups: the training set, the checking set and the testing set for the use of the adaptive neuro-fuzzy inference system (ANFIS). The input vector of ANFIS consists of the yield strength of steel, compressive strength of concrete, dead load, span, width and effective depth of the beam;its outputs are the minimum total cost and optimal steel ratios for positive and negative moments. To make ANFIS more efficient, the technique of Subtractive Clustering is applied to group the data to help streamline the fuzzy rules. Numerical results show that the performance of ANFIS is excellent, with correlation coefficients between the three targets and outputs of the testing data being greater than 0.99.展开更多
This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the ...This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance.展开更多
This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are o...This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10th and 11th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. However, the best result is obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of entire data.展开更多
The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are t...The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are too complicated to be effectively applied to on-line control. In this paper, input-output data and operating experiences will be used to establish PEMFC stack model and operating temperature control system. An adaptive learning algorithm and a nearest-neighbor clustering algorithm are applied to regulate the parameters and fuzzy rules so that the model and the control system are able to obtain higher accuracy. In the end, the simulation and the experimental results are presented and compared with traditional PID and fuzzy control algorithms.展开更多
Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an A...Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System （ANFIS） identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative （PID） and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.展开更多
Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system(ANFIS) based ...Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system(ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.展开更多
Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e....Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.展开更多
The purpose of this research is to demonstrate the use of Adaptive Neuro-Fuzzy Inference System(ANFIS)for discrimination between quarry blasts and microearthquakes in the Tehran region using data from the Broadband Ir...The purpose of this research is to demonstrate the use of Adaptive Neuro-Fuzzy Inference System(ANFIS)for discrimination between quarry blasts and microearthquakes in the Tehran region using data from the Broadband Iranian National Network Center(BIN).In the south and southeast of Tehran,a large number of quarry blasts“contaminate”the earthquake catalog.In order to identify the real seismicity(tectonic earthquakes)in the region,we need to discriminate quarry blasts from natural earthquakes in the catalog.展开更多
基金The National Natural Science Foundation of China under contract No.51379002the Fundamental Research Funds for the Central Universities of China under contract Nos 3132016322 and 3132016314the Applied Basic Research Project Fund of the Chinese Ministry of Transport of China under contract No.2014329225010
文摘An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.
基金Supported by the National Key Basic Research and Development Program of China (2009CB320602)the National Natural Science Foundation of China (60834004, 61025018)+2 种基金the Open Project Program of the State Key Lab of Industrial ControlTechnology (ICT1108)the Open Project Program of the State Key Lab of CAD & CG (A1120)the Foundation of Key Laboratory of System Control and Information Processing (SCIP2011005),Ministry of Education,China
文摘Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.
基金Supported by the Soft Science Program of Jiangsu Province(BR2010079)~~
文摘The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific research institutions. An integrated ANFIS model is built and the effectiveness of the model is verified by means of investigation data and their processing results. The model merges the learning mechanism of neural network and the language inference ability of fuzzy system, and thereby remedies the defects of neural network and fuzzy logic system. Result of this case study shows that the model is suitable for complicated socio-technical systems and has bright application perspective to solve such problems of prediction, evaluation and policy-making in managerial fields.
文摘The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.
基金supported by the National Natural Science Foundation of China(No.50778009)
文摘An adaptive neuro fuzzy inference system was used for classifying water quality status of river.It applied several physical and inorganic chemical indicators including dissolved oxygen,chemical oxygen demand,and ammonia-nitrogen.A data set(nine weeks,total 845 observations)was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model.Up to 89.59%of the data could be correctly classified using this model.Such performance was more competitive when compared with artificial neural networks.It is applicable in evaluation and classification of water quality status.
基金Projects(51108165, 51178170) supported by the National Natural Science Foundation of China
文摘An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demonstrated.Extensive field experimental work was carried out in order to gather enough data for training and prediction.The statistical methods,such as the correlation coefficient,absolute fraction of variance and root mean square error,were given to compare the predicted and actual values for model validation.The simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite accurately.Therefore,the ANFIS approach can reliably be used for forecasting the performance of RUCT.
文摘Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate.The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures(contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.
基金support of China University of Geosciences (Wuhan)
文摘Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method（NATM） is adopted.For this purpose,a systematic convergence measurement is usually implemented to adjust the design during the whole construction,and consequently deadly hazards can be prevented.In this study,a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system（ANFIS） approach.The proposed model used more than 1 000 datasets collected from two different tunnels,i.e.Daguan tunnel No.2 and Yaojia tunnel No.1,which are part of a tunnel located in Hunan Province,China.Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method.The data obtained from Daguan tunnel No.2 were used for model training,while the data from Yaojia tunnel No.1 were employed to evaluate the performance of the model.The input parameters include surrounding rock masses（SRM） rating index,ground engineering conditions（GEC） rating index,tunnel overburden（H）,rock density（？）,distance between monitoring station and working face（D）,and elapsed time（T）.The model’s performance was assessed by the variance account for（VAF）,root mean square error（RMSE）,mean absolute percentage error（MAPE） as well as the coefficient of determination（R2） between measured and predicted data as recommended by many researchers.The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity.
基金The author extends their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IFPSAU-2021/01/18128).
文摘The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.
文摘Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel;design variables are the width and effective depth of the continuous beam and steel ratios for positive and negative moments. The constraints are built based on the ACI Building Code by considering the strength requirements of shear and the maximum positive and negative moments, the development length of flexural reinforcement, and the serviceability requirement of deflection. The objective function is to minimize the total cost of steel and concrete. The optimal data found from the genetic algorithm are divided into three groups: the training set, the checking set and the testing set for the use of the adaptive neuro-fuzzy inference system (ANFIS). The input vector of ANFIS consists of the yield strength of steel, compressive strength of concrete, dead load, span, width and effective depth of the beam;its outputs are the minimum total cost and optimal steel ratios for positive and negative moments. To make ANFIS more efficient, the technique of Subtractive Clustering is applied to group the data to help streamline the fuzzy rules. Numerical results show that the performance of ANFIS is excellent, with correlation coefficients between the three targets and outputs of the testing data being greater than 0.99.
文摘This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance.
文摘This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10th and 11th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. However, the best result is obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of entire data.
文摘The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are too complicated to be effectively applied to on-line control. In this paper, input-output data and operating experiences will be used to establish PEMFC stack model and operating temperature control system. An adaptive learning algorithm and a nearest-neighbor clustering algorithm are applied to regulate the parameters and fuzzy rules so that the model and the control system are able to obtain higher accuracy. In the end, the simulation and the experimental results are presented and compared with traditional PID and fuzzy control algorithms.
文摘Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System （ANFIS） identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative （PID） and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.
基金Project supported by the Brain Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science,ICT,and Future Planning(No.NRF-2017M3C7A1044815)Research Base Construction Fund Support Program funded by Chonbuk National University in 2015
文摘Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system(ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.
文摘Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.
文摘The purpose of this research is to demonstrate the use of Adaptive Neuro-Fuzzy Inference System(ANFIS)for discrimination between quarry blasts and microearthquakes in the Tehran region using data from the Broadband Iranian National Network Center(BIN).In the south and southeast of Tehran,a large number of quarry blasts“contaminate”the earthquake catalog.In order to identify the real seismicity(tectonic earthquakes)in the region,we need to discriminate quarry blasts from natural earthquakes in the catalog.
