The paper presents an approach to identfying a fhzzy model composed of fuzzy-logic rules for a multi-in-put/single outpu system. The ther of fuzzy rules and membership functions of input variables are obtained by mean...The paper presents an approach to identfying a fhzzy model composed of fuzzy-logic rules for a multi-in-put/single outpu system. The ther of fuzzy rules and membership functions of input variables are obtained by means of a fuzzy competitive lerning method with a validity criterion. This method avoids the complexity of system structure identilication and decreases the number of fuzzy rules. Recareive least square algorithm can be used to iden-tify the parameters of conclusion polynomials .The proposed method is used to identify the well-known Box-Jenkins da-ta set with the result shawn at the end of the paper to demonstrae its advanages.展开更多
On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have exc...On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have excellent result in application to clusters of HMM model was also proposed. In combining the parallel, self organizational hierarchical neural networks (PSHNN) to reclassify the scores of every form output by HMM, the CSL speech recognition rate is obviously elevated.展开更多
Introduce a method of generation of new units within a cluster and aalgorithm of generating new clusters. The model automatically builds up its dynamically growinginternal representation structure during the learning ...Introduce a method of generation of new units within a cluster and aalgorithm of generating new clusters. The model automatically builds up its dynamically growinginternal representation structure during the learning process. Comparing model with other typicalclassification algorithm such as the Kohonen's self-organizing map, the model realizes a multilevelclassification of the input pattern with an optional accuracy and gives a strong support possibilityfor the parallel computational main processor. The idea is suitable for the high-level storage ofcomplex datas structures for object recognition.展开更多
Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recogniti...Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this paper we bring forward a new theory, that is, we look the weight sets as target vector sets which is the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and close to the real size of target.展开更多
As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage ...As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage of realizing the time-series community detection by simulating the community formation process.In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set,the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization,parameter optimization and particle domination ability self-adaptive.The experiment result shows that each improved method improves the accuracy of the algorithm,and the F1 score of the improved algorithm is 9.07%higher than that of original algorithm.展开更多
This paper1 addresses different theoretical frameworks of organizational learning （OL） from two aspects： from the perspective of individuals to organizations and from the perspective of organizations to individuals...This paper1 addresses different theoretical frameworks of organizational learning （OL） from two aspects： from the perspective of individuals to organizations and from the perspective of organizations to individuals. The most significant finding is intended to highlight the guidelines for each of researchers＇ concentrated cluster and to demonstrate that different researchers present different guidelines for processes, individual skills, and changes in the environment, teamwork, and competitiveness. The insight, gained by considering OL as a process, is not routine It allows one to create, acquire, and transfer knowledge. This will always be limited to the internal capabilities developed during the course of the timeline and will identify skills and competencies generated in accordance with the requirements presented by different environments. OL is associated with both the change in organizational behaviors and the creation of a knowledge base.展开更多
An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensin...An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensing methods,abundant research has made progress in agronomic parameter retrieval using different CNN frameworks.Nevertheless,limited reports have paid attention to the problems,i.e.,limited measured data,hyperspectral redundancy,and model convergence issues,when concerning CNN models for parameter estimation.Therefore,the present study tried to analyze the effects of synthetic data size expansion employing aGaussian process regression(GPR)model for simulation,input feature optimization using different spectral indices with a competitive adaptive reweighted sampling(CARS)algorithm,model convergence issue combining transfer learning(TL)method for accurate and robust estimation of plant LCC with a deep learning framework(i.e.,ResNet-18)using the ANGERS data(a public dataset containing foliar biochemical parameters spectral data for various plant types).Results showed that ResNet-18 training using 800 simulated reflectances(400–1000 nm)and partial ANGERS data exhibited better results,with an R^(2)value of 0.89,an RMSE value of 6.98μg/cm^(2),an RPD value of 3.70,for LCC retrieval using remanent ANGERS data,thanmodels that using simulations with different amounts of data.The estimation accuracies obviously increased when nine spectral indexes,selected from the CARS algorithm,were used as model input for running the ResNet-18 model(R^(2)=0.96,RMSE=4.65μg/cm^(2),RPD=4.81).In addition,coupling transfer learning with ResNet-18 improved the model convergence rate,and TL-ResNet-18 exhibited accurate results for LCC estimation(R^(2)=0.94,RMSE=5.14μg/cm^(2),RPD=4.65).These results suggest that adding appropriate synthetic data,input features optimization,and transfer learning techniques could be effectively used for improved LCC retrieval with a ResNet-18 model.展开更多
The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the ...The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the policies and teaching demands that formed the basis of this model were analyzed.The study shows the importance of the implementation of the teaching model“promoting teaching and learning through competitions.”This model puts emphasis on the curriculum and teaching resources,while also integrating the teaching process and evaluation with competition.These efforts aim to drive education reform in order to better align with the objectives of vocational education personnel training,while also acting as a reference for similar courses.展开更多
As one of the unsupervised learning models, ART1 has been widely used in data mining or other fields, while coding of it’s learning vector is very important. Their input vector coding methods and learning vector codi...As one of the unsupervised learning models, ART1 has been widely used in data mining or other fields, while coding of it’s learning vector is very important. Their input vector coding methods and learning vector coding methods are described in detail. The corresponding applications are given.展开更多
Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.Wi...Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional展开更多
From the viewpoint of psycholinguistics, this paper concerns how to create an optimal language learning environment in language learning, to stimulate students enthusiasm to participate in classroom activities and t...From the viewpoint of psycholinguistics, this paper concerns how to create an optimal language learning environment in language learning, to stimulate students enthusiasm to participate in classroom activities and to make language learning easier and more pleasant.展开更多
More and more researchers are becoming involved through a variety of actions and programs, which divides assessment into different branches(such as formative, summative, normative assessment, etc). In the present pape...More and more researchers are becoming involved through a variety of actions and programs, which divides assessment into different branches(such as formative, summative, normative assessment, etc). In the present paper, I will try to emphasis that collaborative assessment is positive to high school students' learning outcome, and cooperative and competitive assessment should be mixture implemented in Chinese education process rather than only using competitive assessment to evaluate students' achievement. I hope that Chinese students and teachers could teach or test in the less pressure by reforming the educational evaluation system.展开更多
文摘The paper presents an approach to identfying a fhzzy model composed of fuzzy-logic rules for a multi-in-put/single outpu system. The ther of fuzzy rules and membership functions of input variables are obtained by means of a fuzzy competitive lerning method with a validity criterion. This method avoids the complexity of system structure identilication and decreases the number of fuzzy rules. Recareive least square algorithm can be used to iden-tify the parameters of conclusion polynomials .The proposed method is used to identify the well-known Box-Jenkins da-ta set with the result shawn at the end of the paper to demonstrae its advanages.
基金National Natural Science Foundation ofChina!( No.69672 0 0 7)
文摘On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have excellent result in application to clusters of HMM model was also proposed. In combining the parallel, self organizational hierarchical neural networks (PSHNN) to reclassify the scores of every form output by HMM, the CSL speech recognition rate is obviously elevated.
基金Supported by the National"Fifteenth Year Plan"Key Project(2001BA307B01 02 01)
文摘Introduce a method of generation of new units within a cluster and aalgorithm of generating new clusters. The model automatically builds up its dynamically growinginternal representation structure during the learning process. Comparing model with other typicalclassification algorithm such as the Kohonen's self-organizing map, the model realizes a multilevelclassification of the input pattern with an optional accuracy and gives a strong support possibilityfor the parallel computational main processor. The idea is suitable for the high-level storage ofcomplex datas structures for object recognition.
基金Supported by the Natural Science Foundation of Heilongjiang Province (F0201)
文摘Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this paper we bring forward a new theory, that is, we look the weight sets as target vector sets which is the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and close to the real size of target.
基金This research was funded by National Natural Science Foundation of China(Grant No.2017YFC0820100)。
文摘As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage of realizing the time-series community detection by simulating the community formation process.In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set,the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization,parameter optimization and particle domination ability self-adaptive.The experiment result shows that each improved method improves the accuracy of the algorithm,and the F1 score of the improved algorithm is 9.07%higher than that of original algorithm.
文摘This paper1 addresses different theoretical frameworks of organizational learning （OL） from two aspects： from the perspective of individuals to organizations and from the perspective of organizations to individuals. The most significant finding is intended to highlight the guidelines for each of researchers＇ concentrated cluster and to demonstrate that different researchers present different guidelines for processes, individual skills, and changes in the environment, teamwork, and competitiveness. The insight, gained by considering OL as a process, is not routine It allows one to create, acquire, and transfer knowledge. This will always be limited to the internal capabilities developed during the course of the timeline and will identify skills and competencies generated in accordance with the requirements presented by different environments. OL is associated with both the change in organizational behaviors and the creation of a knowledge base.
基金supported by the National Natural Science Foundation of China(Project Nos.41901268 and 42371385)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002).
文摘An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensing methods,abundant research has made progress in agronomic parameter retrieval using different CNN frameworks.Nevertheless,limited reports have paid attention to the problems,i.e.,limited measured data,hyperspectral redundancy,and model convergence issues,when concerning CNN models for parameter estimation.Therefore,the present study tried to analyze the effects of synthetic data size expansion employing aGaussian process regression(GPR)model for simulation,input feature optimization using different spectral indices with a competitive adaptive reweighted sampling(CARS)algorithm,model convergence issue combining transfer learning(TL)method for accurate and robust estimation of plant LCC with a deep learning framework(i.e.,ResNet-18)using the ANGERS data(a public dataset containing foliar biochemical parameters spectral data for various plant types).Results showed that ResNet-18 training using 800 simulated reflectances(400–1000 nm)and partial ANGERS data exhibited better results,with an R^(2)value of 0.89,an RMSE value of 6.98μg/cm^(2),an RPD value of 3.70,for LCC retrieval using remanent ANGERS data,thanmodels that using simulations with different amounts of data.The estimation accuracies obviously increased when nine spectral indexes,selected from the CARS algorithm,were used as model input for running the ResNet-18 model(R^(2)=0.96,RMSE=4.65μg/cm^(2),RPD=4.81).In addition,coupling transfer learning with ResNet-18 improved the model convergence rate,and TL-ResNet-18 exhibited accurate results for LCC estimation(R^(2)=0.94,RMSE=5.14μg/cm^(2),RPD=4.65).These results suggest that adding appropriate synthetic data,input features optimization,and transfer learning techniques could be effectively used for improved LCC retrieval with a ResNet-18 model.
文摘The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the policies and teaching demands that formed the basis of this model were analyzed.The study shows the importance of the implementation of the teaching model“promoting teaching and learning through competitions.”This model puts emphasis on the curriculum and teaching resources,while also integrating the teaching process and evaluation with competition.These efforts aim to drive education reform in order to better align with the objectives of vocational education personnel training,while also acting as a reference for similar courses.
文摘As one of the unsupervised learning models, ART1 has been widely used in data mining or other fields, while coding of it’s learning vector is very important. Their input vector coding methods and learning vector coding methods are described in detail. The corresponding applications are given.
文摘Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional
文摘From the viewpoint of psycholinguistics, this paper concerns how to create an optimal language learning environment in language learning, to stimulate students enthusiasm to participate in classroom activities and to make language learning easier and more pleasant.
文摘More and more researchers are becoming involved through a variety of actions and programs, which divides assessment into different branches(such as formative, summative, normative assessment, etc). In the present paper, I will try to emphasis that collaborative assessment is positive to high school students' learning outcome, and cooperative and competitive assessment should be mixture implemented in Chinese education process rather than only using competitive assessment to evaluate students' achievement. I hope that Chinese students and teachers could teach or test in the less pressure by reforming the educational evaluation system.
