神经网络模糊同步算法研究+文献综述

摘要模糊神经网络是模糊控制和神经网络相结合的产物，具有较强的自组织能力和自学习能力，其稳定性和同步方面的研究成果在很多领域存在着潜在的应用,如军工、农业、通信等。本文首先对模糊神经网络的国内外发展现状进行了简要介绍，在此基础上研究了两个含有状态时滞的神经网络系统通过线性增益控制器以及参数自适应控制器的同步问题，给出了系统实现同步的充分条件。进而引出含状态时滞的模糊神经网络系统通过模糊控制器的控制来实现同步的情况，其中响应系统采用模糊模型，而驱动系统为一般时滞神经网络系统，同时控制器的参数采用自适应形式。分析时建立Lyapunov函数，通过计算误差系统的稳定性判据，进而得到两系统同步的充分条件。最后给出一些数值例子利用MATLAB软件对分析结果进行仿真，对其进行了验证。本文的研究成果适用于驱动系统参数未知或具有不确定性的情况下的同步控制。19576
关键词神经网络，模糊控制，同步，Lyapunov方法，自适应控制
毕业设计说明书（论文）外文摘要
Title Research on synchronization of fuzzy neural networks s

Abstract
Fuzzy neural networks (FNNs) are combinations of fuzzy control and neural network, which are good at self organization and self-learning. The research results of FNNs are well applied in many fields, such as military industries, agriculture and communication, etc. At first, this thesis briefly introduces the development status of FNNs. And on this basis, sufficient conditions for synchronization of two neural network systems with state time-delay through linear feedback gain controller and parameter self-adapting controller are prposed. Then the case that fuzzy neural network systems with state time delay realize synchronization through fuzzy controller is considered. The response system is designed as fuzzy system, while the drive system designed as the conventional time-delayed neural network system. The parameters of the controller are adopted in the adaptive form at the same time. During the research, some Lyapunov functions are proposed to study the synchronization problems. Based on the analysis of error system stability criterion, some sufficient conditions to ensure the considered systems achieving synchronization are provided. The proposed criteria which are in terms of LMIs can be testified based on the result of the MATLAB simulation for a few numerical examples. The results of this thesis are suitable for the case that the parameters of drive system are of uncertainty or unknown.
Keywords Neural network, fuzzy control, synchronization, Lyapunov method, adaptive control
目次
1 引言    1
1.1 模糊神经网络概述    1
1.2 混沌同步概述    3
1.3 本文的主要工作及研究意义    3
2 预备知识    5
2.1 Lyapunov稳定性定理    5
2.2 Lasalle不变性定理    6
2.3自适应控制    7
2.4 MATLAB仿真    7
2.5 神经网络系统    8
2.6符号说明    8
3 时滞神经网络同步分析    10
3.1 同步控制    10
3.2 时滞系统同步分析    10
3.3 数值仿真    18
4 时滞模糊神经网络自适应同步    25
4.1模糊控制系统    25
4.2 时滞线性模糊系统自适应同步    26
4.3 时滞模糊神经网络自适应同步    33
4.4 数值仿真    44
5 研究中出现的问题和解决方法    63
结论    64
致谢    65
参考文献    66 神经网络模糊同步算法研究+文献综述:http://www.751com.cn/zidonghua/lunwen_10996.html