Abstract
Determination of appropriate neural-network (NN) structure is an important issue for a given learning or training task since the NN performance depends much on it. To remedy the weakness of conventional BP neural networks and learning algorithms, a new Laguerre orthogonal basis neural network is constructed. Based on this special structure, a weights-direct-determination method is derived, which could obtain the optimal weights of such a neural network directly (or to say, just in one step). Furthermore, a growing algorithm is presented for determining immediately the smallest number of hidden-layer neurons. Theoretical analysis and simulation results substantiate the efficacy of such a Laguerre-orthogonal-basis neural network and its growing algorithm based on the weights-direct-determination method.
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Zhang, Y., Zhong, T., Li, W., Xiao, X., Yi, C. (2008). Growing Algorithm of Laguerre Orthogonal Basis Neural Network with Weights Directly Determined. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_8
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DOI: https://doi.org/10.1007/978-3-540-85984-0_8
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