Titlebook: Advances in Neural Networks - ISNN 2006; Third International Jun Wang,Zhang Yi,Hujun Yin Conference proceedings 2006 Springer-Verlag Berli

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Qianbin Chen,Weixiao Meng,Liqiang Zhaocognitive processes. However, several current models incorporated learning algorithms that apparently have questionable descriptive validity or qualitative plausibleness. The present research attempts to bridge this gap by identifying five critical issues overlooked by previous modeling research and

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Yingjie Wang,Wei Luo,Changxiang Shenon of functions is developed by using integral transform. Using the developed representation, an approximation order estimation for the bell-shaped neural networks is obtained. The obtained result reveals that the approximation accurately of the bell-shaped neural networks depends not only on the nu

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Terence R. Cannings,Sue G. Talleynsity or upper bound estimation on how a multivariate function can be approximated by the networks, and consequently, the essential approximation ability of networks cannot be revealed. In this paper, by establishing both upper and lower bound estimations on approximation order, the essential approx

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Communications in an era of networksis a linear combination of wavelets, that can be updated during the networks training process. As a result the approximate error is significantly decreased. The BP algorithm and the QR decomposition based training method for the proposed WNN is derived. The obtained results indicate that this new ty

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Online university degree programmesof diffusion operator and the techniques of inequality, we investigate positive invariant set, global exponential stability, and then obtain the exponential dissipativity of the neural networks under consideration. Our results can extend and improve earlier ones. An example is given to demonstrate t