Convergence of an online split-complex gradient algorithm for complex-valued neural networks.

Zhang, Huisheng; Xu, Dongpo; Wang, Zhiping

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The UD RLS algorithm for training feedforward neural networks

Jarosław Bilski (2005)

International Journal of Applied Mathematics and Computer Science

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A new algorithm for training feedforward multilayer neural networks is proposed. It is based on recursive least squares procedures and U-D factorization, which is a well-known technique in filter theory. It will be shown that due to the U-D factorization method, our algorithm requires fewer computations than the classical RLS applied to feedforward multilayer neural network training.

Modelling of the automatic depth control electrohydraulic system using RBF neural network and genetic algorithm.

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Boundedness and stability for discrete-time delayed neural network with complex-valued linear threshold neurons.

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Discrete Dynamics in Nature and Society

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Hamming star-convexity packing in information storage.

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Fixed Point Theory and Applications [electronic only]

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Comparison of supervised learning methods for spike time coding in spiking neural networks

Andrzej Kasiński, Filip Ponulak (2006)

International Journal of Applied Mathematics and Computer Science

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In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods?...