Title

一种基于Walsh变换的反馈过程神经网络模型及学习算法

Translated Titles

A Feedback Procedure Neural Network Model Based on Walsh Conversion and Its Learning Algorithm

Authors

许增福(Zeng-Fu Xu);梁静国(Jing-Guo Liang);李盼池(Pan-Chi Li);许少华(Shao-Hua Xu)

Key Words

过程神经元 ; 反馈网络 ; Walsh变换 ; 学习算法 ; procedure neuron ; feedback network ; Walsh conversion ; learning algorithm

PublicationName

信息與控制

Volume or Term/Year and Month of Publication

33卷4期(2004 / 08 / 01)

Page #

404 - 407

Content Language

簡體中文

Chinese Abstract

提出了一种带有反馈输入的过程式神经元网络模型,模型为三层结构,其隐层和输出层均为过程神经元。输入层完成连续信号的输入,隐层完成输入信号的空间聚合和向输出层逐点映射,并将输出信号逐点反馈到输入层;输出层完成隐层输出信号的时、空聚合运算和系统输出。在对权函数实施Walsh变换的基础上给出了该模型的学习算法。仿真实验证明了模型和算法的有效性。

English Abstract

A feedback procedure neural network model (FPNN) is proposed. The FPNN has three layers, and its hidden layer and output layer are composed of procedure neurons. The input layer accomplishes continuous signal input, while the hidden layer accomplishes input signal aggregation in space and transfers the input signals to the output layer. Then the hidden layer transfers its own output to the input layer, both point by point. The output layer accomplishes output signal aggregation in both space and time, and fulfills system output. A learning algorithm is presented based on Walsh conversion of weight function. Simulation experiment proves the availability and effectiveness of the model and algorithm.

Topic Category 基礎與應用科學 > 資訊科學
工程學 > 土木與建築工程
工程學 > 電機工程