Fourier三角基神经元网络的权值直接确定法  

A Direct-Weight-Determination Method for Trigonometrically-Activated Fourier Neural Networks

作　　者：张雨浓[1] 旷章辉[1] 肖秀春[1,2] 陈柏桃[3] 

机构地区：[1]中山大学信息科学与技术学院,广东广州510275 [2]广东海洋大学信息学院,广东湛江524088 [3]广东海洋大学航海教育质管办,广东湛江524088

出　　处：《计算机工程与科学》2009年第5期112-115,共4页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(60643004;60775050);中山大学科研启动费;后备重点课题资助项目

摘　　要：根据Fourier变换理论,本文构造出一类基于三角正交基的前向神经网络模型。该模型由输入层、隐层、输出层构成,其输入层和输出层采用线性激励函数,以一组三角正交基为其隐层神经元的激励函数。依据误差回传算法(即BP算法),推导了权值修正的迭代公式。针对BP迭代法收敛速度慢、逼近目标函数精度较低的缺点,进一步提出基于伪逆的权值直接确定法,该方法避免了权值反复迭代的冗长过程。仿真和预测结果表明,该方法比传统的BP迭代法具有更快的计算速度和更高的仿真与测试精度。Based on the Fourier transformation theory, a feed-forward neural network using trigonometric orthogonal activation-functions is constructed in this paper. The neural network adopts a three-layer structure, where the input and output layers employ linear activation functions, while the hidden-layer neurons are activated by a series of trigonometric orthogonal functions. In this paper, we first derive its weight-updating formula by adopting the standard BP training algorithrn. More importantly, a pseudo-inverse method is proposed as well, which directly determines the weights of the neural network without iterative BP training. Simulation results show that the direct-weight-determination method is more efficient and accurate than the conventional BP iterative-training algorithms.

关 键 词：三角正交基函数 Fourier三角基神经元网络 权值修正 直接确定法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]