Title

基于模糊进化规划和分层方法的神经网络设方法

Translated Titles

A Neural Network Design Method Based on Fuzzy Evolutionary Programming and Layer-wise Method

Authors

刘芳(Fang Liu);李人厚(Ren-Hou Li)

Key Words

模糊进化规划 ; 前向神经网络 ; 分层方法 ; fuzzy evolutionary programming ; feedforward neural network ; layer-wise method

PublicationName

信息與控制

Volume or Term/Year and Month of Publication

33卷4期(2004 / 08 / 01)

Page #

385 - 388

Content Language

簡體中文

Chinese Abstract

本文提出一种模糊进化规划,用于前向神经网络的设计。该方法通过对神经元的部分解群体的进化,缩短了个体的编码长度,显著地减轻了计算量,同时这种方法不但能够在很大程度上简化适应值的计算,更重要的是能够降低适应值空间的复杂性,从而能够加速进化算法收敛到全局最优点。仿真结果显示,本文提出的算法能够有效抑制进化规划算法初期收敛的发生,有效地提高多层前向神经网络收敛精度,并可获得更为简洁的网络结构。

English Abstract

A fuzzy evolutionary programming method to design the feedforward neural network is proposed. By evolving a population of neurons instead of neural, the length of coding is decreased and computation pressure is greatly alleviated. At the same time, the method not only simplifies the computation of the fitness, but also decreases the complexity of the fitness space. The simulation results show that the premature convergence in evolutionary programming is restrained effectively, and the learning efficiency and convergence precision for the weights of the multi-layer feedforward neural networks are improved greatly. These results also show that the proposed method can produce very compact artificial neural networks in comparison with other algorithms.

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