Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented ...Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.展开更多
We present a neuro-heuristic computing platform for finding the solution for initial value problems （IVPs） of non- linear pantograph systems based on functional differential equations （P-FDEs） of different orders....We present a neuro-heuristic computing platform for finding the solution for initial value problems （IVPs） of non- linear pantograph systems based on functional differential equations （P-FDEs） of different orders. In this scheme, the strengths of feed-forward artificial neural networks （ANNs）, the evolutionary computing technique mainly based on genetic algorithms （GAs）, and the interior-point technique （IPT） are exploited. Two types of mathematical models of the systems are constructed with the help of ANNs by defining an unsupervised error with and without exactly satisfying the initial conditions. The design parameters of ANN models are optimized with a hybrid approach GA-IPT, where GA is used as a tool for effective global search, and IPT is incorporated for rapid local convergence. The proposed scheme is tested on three different types oflVPs of P-FDE with orders 1-3 The correctness of the scheme is established by comparison with the existing exact solutions. The accuracy and convergence ofthc proposed scheme are further validated through a large number of numerical experiments by taking different numbers of neurons in ANN models.展开更多
基金Supported by the National Natural Science Foundation of China (No. 30570485)the Shanghai "Chen Guang" Project (No. 09CG69).
文摘Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.
文摘We present a neuro-heuristic computing platform for finding the solution for initial value problems （IVPs） of non- linear pantograph systems based on functional differential equations （P-FDEs） of different orders. In this scheme, the strengths of feed-forward artificial neural networks （ANNs）, the evolutionary computing technique mainly based on genetic algorithms （GAs）, and the interior-point technique （IPT） are exploited. Two types of mathematical models of the systems are constructed with the help of ANNs by defining an unsupervised error with and without exactly satisfying the initial conditions. The design parameters of ANN models are optimized with a hybrid approach GA-IPT, where GA is used as a tool for effective global search, and IPT is incorporated for rapid local convergence. The proposed scheme is tested on three different types oflVPs of P-FDE with orders 1-3 The correctness of the scheme is established by comparison with the existing exact solutions. The accuracy and convergence ofthc proposed scheme are further validated through a large number of numerical experiments by taking different numbers of neurons in ANN models.
