This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that th...This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.展开更多
Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane h...Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm （DHPA1NC） is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.展开更多
The Dirac symbol is used to represent the discrete complex Hopfield neural network model.The signal-to-noise theory and the computer numerical solution are made to analyse the storage capacity of the model.The storage...The Dirac symbol is used to represent the discrete complex Hopfield neural network model.The signal-to-noise theory and the computer numerical solution are made to analyse the storage capacity of the model.The storage capacity ratio of the model equals to that of the Hopfield model.Finally,using the model to recognize the 4-level grey or color patterns is discussed.展开更多
Traditional cheaptalk game model with homogeneous information sources provided a con clusion that dishonest information sources will not be identified if he changes strategy stochastically. In this paper, the authors ...Traditional cheaptalk game model with homogeneous information sources provided a con clusion that dishonest information sources will not be identified if he changes strategy stochastically. In this paper, the authors incorporate different information diffusion networks and heterogeneous in formation sources into an agentbased artificial stock market. The obtained results are different with traditional results that identification ability of uninformed agents has been highly improved with diffu sion networks and heterogeneous information sources. Additionally, the authors find uninformed agents can improve identification ability only if there exists a sufficient number of heterogeneous information sources in stock market.展开更多
文摘This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs.
基金Foundation item： Projects（61170199, 60874070） supported by the National Natural Science Foundation of China Project（11A004） supported by the Major Project of Education Department in Hunan Province, China Project（2010GK3067） supported by Science and Technology Planning of Hunan Province, China
文摘Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm （DHPA1NC） is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.
文摘The Dirac symbol is used to represent the discrete complex Hopfield neural network model.The signal-to-noise theory and the computer numerical solution are made to analyse the storage capacity of the model.The storage capacity ratio of the model equals to that of the Hopfield model.Finally,using the model to recognize the 4-level grey or color patterns is discussed.
基金supported by the National Natural Science Foundation of China under Grant Nos.71131007,71271144,and 71271145the New Century Excellent Talents Supporting Program by Ministry of Education under Grant No.NECT-10-0626the Innovative Research Team in University Supporting Program by Ministry of Education under Grant No.IRT 1208
文摘Traditional cheaptalk game model with homogeneous information sources provided a con clusion that dishonest information sources will not be identified if he changes strategy stochastically. In this paper, the authors incorporate different information diffusion networks and heterogeneous in formation sources into an agentbased artificial stock market. The obtained results are different with traditional results that identification ability of uninformed agents has been highly improved with diffu sion networks and heterogeneous information sources. Additionally, the authors find uninformed agents can improve identification ability only if there exists a sufficient number of heterogeneous information sources in stock market.
