指数重み付き最小2乗法によるEBP学習アルゴリズム

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  • EBP Learning Algorithm Using an Exponential Weighted Least Squares Method

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A new error back propagation method which is called EBP learning algorithm has been proposed by the one of the authors for a supervised learning of multilayer neural networks. This method is free from a gradient method which is used in the well known BP method or its improved versions. The method is composed of the following two stages.<br>A) Error back propagation: Determination of fictitious teacher signals of hidden layers from the output error of a neural network in the backward direction.<br>B) Weight parameters determination: Weight parameters of a neural network should be determined to minimize the fictitious error using fictitious teacher signals of each hidden layer.<br>An exponential weighted least squares (EWLS) method which is well known in the field of signal processing is proposed in this paper to use for a determination of weight parameters of B). Simulation results using a proposed EBP-EWLS algorithm for a learning of a sinusoidal function are presented. The results show the advantge of learning numbers for success and 100% convergence rate for initial values of weight parameters of a neural network.

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