Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid...Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid some disadvantages of rule-based expert system. The main objective of the system is to assist die casting engineer in identifying defect, determining the probable causes of defect and proposing remedies to eliminate the defect. 14 common die casting defects could be identified quickly by expert system on the basis of their characteristics. BP neural network in combination with expert system was applied to map the complex relationship between causes and defects, and further explained the cause determination process. Cause determination gives due consideration to practical process conditions. Finally, corrective measures were recommended to eliminate the defect and implemented in the sequence of difficulty.展开更多
The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw wate...The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, PH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Based on neural network and rule models, an expert system for determining the optimum chemical dosage rate is developed and used in a water treatment work, and the results of actual runs show that in the condition of satisfying the demand of drinking water quality, the usage of coagulant is lowered.展开更多
In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty ...In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.展开更多
In this paper, we propose a formal definition, general structure and work principle of the Neural Network Expert System (NNES) based on joint-type knowledge representation, and show a practical application example usi...In this paper, we propose a formal definition, general structure and work principle of the Neural Network Expert System (NNES) based on joint-type knowledge representation, and show a practical application example using NNES for forecasting the water invasion of coal mine.展开更多
The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrati...The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.展开更多
Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process....Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process. This paper discusses the principle of a hybrid system of a neural network and an expert system (HNNES), i.e., knowledge representation, reasoning mechanism, and knowledge acquisition based on neural networks. An architecture of HNNES is presented in consideration of the feature of the design of electrical machines.展开更多
In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule sampl...In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.展开更多
By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a...By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a rule reasoning expert system; it can also make use of all kinds of knowledge in the repository to diagnose the fault with the positive and negative mixing reasoning mode. The binary system was adopted to denote all kinds of fault in a rotation mechanism. The neural networks were trained with a random parallel algorithm (Alopex). The expert system overcomes the self learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network. The expert system developed in this paper has powerful diagnosing ability.展开更多
Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this pap...Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.展开更多
An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in mat...An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.展开更多
Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault qu...Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic ele- ment is the test-diagnosis fault unit. Every underlying fault cause＇s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.展开更多
In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuz...In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.展开更多
In order to improve the detection efficiency of rule-based expert systems, an intrusion detection approach using connectionist expert system is proposed. The approach converts the AND/OR nodes into the corresponding n...In order to improve the detection efficiency of rule-based expert systems, an intrusion detection approach using connectionist expert system is proposed. The approach converts the AND/OR nodes into the corresponding neurons, adopts the three layered feed forward network with full interconnection between layers, translates the feature values into the continuous values belong to the interval [0, 1], shows the confidence degree about intrusion detection rules using the weight values of the neural networks and makes uncertain inference with sigmoid function. Compared with the rule based expert system, the neural network expert system improves the inference efficiency.展开更多
The samples obtained by Finite Element Method (FEM) simulation for section extrusion process have been trained on BP Neural Networks. The mapping relationsbetween die's geometrical parameters and energetic paramet...The samples obtained by Finite Element Method (FEM) simulation for section extrusion process have been trained on BP Neural Networks. The mapping relationsbetween die's geometrical parameters and energetic parameters, such as stress and strain generated in the die are established. The extrusion process model and its expert system are also determined. The excellent expansibility this system possesses provides a new prospect for the future development of expert system for section extrusion dies.展开更多
Prediction of surface finish in turning process is a difficult but important task. Artificial Neural Networks (ANN) can reliably pred ict the surface finish but require a lot of training data. To overcome this prob le...Prediction of surface finish in turning process is a difficult but important task. Artificial Neural Networks (ANN) can reliably pred ict the surface finish but require a lot of training data. To overcome this prob lem, an expert system approach is proposed, wherein it will be possible to predi ct the surface finish from limited experiments. The expert system contains a kno wledge base prepared from machining data handbooks and number of experiments con ducted by turning steel rods, over a wide range of cutting parameters. With this knowledge base, the expert system predicts surface finish for different tool-w ork-piece combinations, by carrying out few experiments for each case. The prop osed expert system model is validated by carrying out a number of experiments.展开更多
A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in th...A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.展开更多
Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical mod...Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.展开更多
The maintenance and forecast expert system of equipment based on Artificial Neural Network is composed of control, measure, failure forecast, execution, data processing module and database. The data processing module ...The maintenance and forecast expert system of equipment based on Artificial Neural Network is composed of control, measure, failure forecast, execution, data processing module and database. The data processing module obtains the change of the controlled objects＇ structure and parameters, then takes correspondent measures according to the examination and diagnosis information. The failure forecast module finds the control system fault, separates the fault symptom location, tells the fault kind, estimates the magnitude and time of the fault, and finally makes evaluation and decision.展开更多
文摘Due to the competition and high cost associated with die casting defects, it is urgent to adopt a rapid and effective method for defect analysis. In this research, a novel expert network approach was proposed to avoid some disadvantages of rule-based expert system. The main objective of the system is to assist die casting engineer in identifying defect, determining the probable causes of defect and proposing remedies to eliminate the defect. 14 common die casting defects could be identified quickly by expert system on the basis of their characteristics. BP neural network in combination with expert system was applied to map the complex relationship between causes and defects, and further explained the cause determination process. Cause determination gives due consideration to practical process conditions. Finally, corrective measures were recommended to eliminate the defect and implemented in the sequence of difficulty.
基金This work was supported by the project 863 ofChina(No.863-511092)
文摘The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, PH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Based on neural network and rule models, an expert system for determining the optimum chemical dosage rate is developed and used in a water treatment work, and the results of actual runs show that in the condition of satisfying the demand of drinking water quality, the usage of coagulant is lowered.
文摘In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.
文摘In this paper, we propose a formal definition, general structure and work principle of the Neural Network Expert System (NNES) based on joint-type knowledge representation, and show a practical application example using NNES for forecasting the water invasion of coal mine.
文摘The most important parameters which control the electrolytic process are the concentrations of zinc and sulfuric acid in the electrolyte. An expert control strategy for determining and tracking the optimal concentrations was proposed, which uses neural networks, rule models and a single loop control scheme. First, the process was described and the strategy that features an expert controller and three single loop controllers was explained. Next, neural networks and rule models were constructed based on statistical data and empirical knowledge on the process. Then, the expert controller for determining the optimal concentrations was designed through a combination of the neural networks and rule models. The three single loop controllers used the PI algorithm to track the optimal concentrations. Finally, the implementation of the proposed strategy were presented. The run results show that the strategy provides not only high purity metallic zinc, but also significant economic benefits.
文摘Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process. This paper discusses the principle of a hybrid system of a neural network and an expert system (HNNES), i.e., knowledge representation, reasoning mechanism, and knowledge acquisition based on neural networks. An architecture of HNNES is presented in consideration of the feature of the design of electrical machines.
文摘In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.
文摘By combining the artificial neural network with the rule reasoning expert system, an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a rule reasoning expert system; it can also make use of all kinds of knowledge in the repository to diagnose the fault with the positive and negative mixing reasoning mode. The binary system was adopted to denote all kinds of fault in a rotation mechanism. The neural networks were trained with a random parallel algorithm (Alopex). The expert system overcomes the self learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network. The expert system developed in this paper has powerful diagnosing ability.
基金Supported in part by the National Natural Science Foundation of China (No.60272046, No.60102011), Na-tional High Technology Project of China (No.2002AA143010), Natural Science Foundation of Jiangsu Province (No.BK2001042), and the Foundation for Excellent Doctoral Dissertation of Southeast Univer-sity (No.YBJJ0412).
文摘Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.
文摘An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
基金The 11th Five-year National Defense Preliminary Research Projects (B0520060455)
文摘Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic ele- ment is the test-diagnosis fault unit. Every underlying fault cause＇s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.
文摘In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.
文摘In order to improve the detection efficiency of rule-based expert systems, an intrusion detection approach using connectionist expert system is proposed. The approach converts the AND/OR nodes into the corresponding neurons, adopts the three layered feed forward network with full interconnection between layers, translates the feature values into the continuous values belong to the interval [0, 1], shows the confidence degree about intrusion detection rules using the weight values of the neural networks and makes uncertain inference with sigmoid function. Compared with the rule based expert system, the neural network expert system improves the inference efficiency.
文摘The samples obtained by Finite Element Method (FEM) simulation for section extrusion process have been trained on BP Neural Networks. The mapping relationsbetween die's geometrical parameters and energetic parameters, such as stress and strain generated in the die are established. The extrusion process model and its expert system are also determined. The excellent expansibility this system possesses provides a new prospect for the future development of expert system for section extrusion dies.
文摘Prediction of surface finish in turning process is a difficult but important task. Artificial Neural Networks (ANN) can reliably pred ict the surface finish but require a lot of training data. To overcome this prob lem, an expert system approach is proposed, wherein it will be possible to predi ct the surface finish from limited experiments. The expert system contains a kno wledge base prepared from machining data handbooks and number of experiments con ducted by turning steel rods, over a wide range of cutting parameters. With this knowledge base, the expert system predicts surface finish for different tool-w ork-piece combinations, by carrying out few experiments for each case. The prop osed expert system model is validated by carrying out a number of experiments.
基金Supported by the Climbing Programme-National Key Project for Fundamental Research in China, Grant NSC92097
文摘A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.
文摘Fault detection and diagnosis for pneumatic system of automatic productionline are studied. An expert system using fuzzy-neural network and pneumatic circuit fault diagnosisinstrument are deigned. The mathematical model of various pneumatic faults and experimental deviceare built. In the end, some experiments are done, which shows that the expert system usingfuzzy-neural network can diagnose fast and truly fault of pneumatic circuit.
文摘The maintenance and forecast expert system of equipment based on Artificial Neural Network is composed of control, measure, failure forecast, execution, data processing module and database. The data processing module obtains the change of the controlled objects＇ structure and parameters, then takes correspondent measures according to the examination and diagnosis information. The failure forecast module finds the control system fault, separates the fault symptom location, tells the fault kind, estimates the magnitude and time of the fault, and finally makes evaluation and decision.
