Fast convergence algorithm for wavelet neural network used for signal or function approximation

Song Xiangyu, Qi Feihu
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引用次数: 2

Abstract

A new way to set the initial values of the wavelet neural network's parameters is proposed in order to improve the convergence speed. Experiments on linear polynomials, exponent functions, sin & cos functions and a certain multistage simulation function show the neural network has a much faster convergence speed and can be widely used for approximating many kinds of signals and functions. A discussion on the merit of this method is given. The experiment results are satisfactory.
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小波神经网络用于信号或函数逼近的快速收敛算法
为了提高小波神经网络的收敛速度,提出了一种设置小波神经网络参数初值的新方法。对线性多项式、指数函数、sin和cos函数以及某多阶段仿真函数的实验表明,神经网络具有更快的收敛速度,可广泛用于逼近多种信号和函数。对该方法的优点进行了讨论。实验结果令人满意。
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