Advances in Neural Networks - ISNN 2004: International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part 2
Fuliang Yin, Jun Wang, Chengan Guo
Springer, Oct 5, 2004 - Neural computers - 1021 pages
The two volume set LNCS 3173/3174 constitutes the refereed proceedings of the International Symposium on Neural Networks, ISNN 2004, held in Dalian, China in August 2004. The 329 papers presented were carefully reviewed and selected from more than 800 submissions. The papers span the entire scope of neural computing and its applications; they are organized in 11 major topical parts on theoretical analysis; learning and optimization; support vector machines; blind source separation, independent component analysis, and principal component analysis; clustering and classification; robotics and control; telecommunications; signal image, and time series analysis; biomedical applications; detection, diagnosis, and computer security; and other applications.
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RBFNNBased Multiple Steady States Controller
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Adaboost applications approach artificial neural network audio beamforming Berlin Heidelberg 2005 binary Blind Source Separation chaotic China classification algorithm clustering coefficients Computer corresponding data set database defined denotes detection discriminant eigenvectors encryption equation error estimate experimental results experiments face recognition feature extraction feature space feature vector filter frequency function fuzzy Gaussian Heidelberg hidden layer IEEE IEEE Trans Independent Component Independent Component Analysis input vector ISNN iterative kernel Liao linear LNCS matrix module neural network neuron nodes noise nonlinear obtained optimal original image output layer paper parameters Pattern Recognition performance pixels prediction problem Proc proposed method prototype pattern PSNR recognition rate region robustness scheme selected Self-Organizing Map sequence shows Signal Processing simulation source signals speech Springer-Verlag Berlin Heidelberg statistical structure Support Vector Machines Table technique tion training set transform visual Wang watermark wavelet weight Yi Eds