【作者】 韩霞；

【导师】 刘军；

【作者基本信息】 西安理工大学 ， 检测技术与自动化装置， 2004， 硕士

【摘要】 模糊神经网络是将人工神经网络与模糊逻辑系统相结合的一种能处理抽象信息的网络，具有强大的自学习和自整定功能，是智能控制理论研究领域中一个十分活跃的分支，因此模糊神经网络控制的研究具有重要的意义。本文在分析模糊神经网络理论和应用现状的基础上，针对模糊神经网络控制中存在的一些问题，主要进行以下两个方面的研究： 1．针对模糊神经网络控制器一般存在着在线权值调整计算量大、训练时间长、过度修正权值可能导致系统剧烈振荡等缺点，提出了两种模糊神经网络控制器的优化方法：在线自学习过程中仅对控制性能影响大的控制规则相关的权值进行修正，以减小计算量，加快训练速度；基于T-S模糊模型，根据偏差及偏差变化率大小动态自适应调节权值修正步长，抑制控制器输出的剧烈变化，避免系统发生剧烈振荡。仿真结果表明本文提出的优化方法能大大减少在线权值修正的计算量，加快了系统的收敛速度。 2．由于传统控制器本身的局限，它们在非线性控制系统的设计和应用中存在许多问题，本文将模糊神经网络和传统控制策略相结合，设计了两种模糊神经网络自适应控制器：基于模糊基函数网络的间接型稳定自适应控制器和基于T-S模糊神经网络直接型稳定自适应控制器。首先用模糊神经网络西安理工大学硕士学位论文完成对控制系统未知结构或参数的逼近，然后根据Lyapunov稳定性定理设计网络参数的自适应学习律，在线完成网络参数的调整，使系统满足lyapunov稳定性。仿真结果表明，这两种控制器都能很好地实现跟踪输入信号，并满足系统稳定性的要求。更多还原

【Abstract】 Fuzzy neural network (FNN) is an active branch in the intelligent control. It is composed of the neural network and fuzzy logic system organically. And it is good at self-learning and self-tuning. So the theory of FNN is very important for the intelligent control. But there are still some problems in it. In this dissertation, the following theories and application of FNN are discussed.1. The large account of compute work of updating weights and long training time usually discourage the FNN’ s on-line application in industry. Moreover, when it is trained on-line to adapt to plant variations, the over-tuned may cause system oscillate extensively. In this dissertation, two kinds of optimization, methods are proposed. Firstly, only these linking weights corresponding to the control rules that affect the control performance significantly are updated in order to reduce the compute works and speed up the training progress. Secondly, the updating step is adjusted adaptively in accordance with the error and the change of error of the system based on the T-S model to get better performance. Simulation results show that the training time is reduced greatly and the convergence velocity is speed up.2. It is difficult to design a traditional controller for some nonlinearsystems, as some parts of the system are unknown. In this dissertation, the FNN and the traditional controller are combined to design two kinds of FNN adaptive controllers for a class of nonlinear plant. They are stable indirect adaptive controller based on fuzzy basic function network and stable direct adaptive controller based on T-S fuzzy neural network. The FNN in the system is used to approximate the unknown parts of the system. Then according to the Lyapunov’s stability theory, the adaptive laws of parameters are designed. The parameters of network are adjusted on line and the system satisfies the Lyapunov’s stability. The simulation results show that these two kinds of adaptive controllers can realize tracing the input signal commendably and satisfy the request of stability simultaneously.更多还原