【作者】 孙海蓉；

【导师】 韩璞；

【作者基本信息】 华北电力大学（河北） ， 热能工程， 2006， 博士

【摘要】 模糊神经网络控制是智能控制理论中一个十分活跃的分支。虽然模糊神经网络在复杂系统控制和建模等应用中已经取得了很多的成就,但是它在理论和应用中仍然存在一些问题。本论文对其中一些问题进行了研究:模糊神经网络的学习能力、模糊规则的自动获取、模糊逻辑和神经网络技术的结合方式、模糊神经网络用于复杂系统的辨识,以及模糊神经网络用于复杂系统的控制。本论文的工作包括:1、评述了模糊神经网络用于复杂系统辨识和控制的研究现状,并且指出了它在理论和应用中存在的问题。2、针对以上存在问题,对模糊神经网络进行研究。对多种建立模糊神经方法进行了分析比较,并提出了一种易于实现的新的模糊神经系统建立方法,解决了模糊系统建立过程中的模糊规则自动生成的问题。3、基于本文提出的模糊神经系统建立方法,提出了新的模糊神经系统辨识方法。实验结果表明,与传统的模糊系统相比,提高了辨识精度,可以解决BP学习算法存在的收敛速度慢、局部极小等问题,而且系统更稳定。4、提出了一种针对非线性系统的自适应模糊神经网络广义预测控制方法。这种算法能解决对控制信号的范围和变化率带约束条件的非线性系统的预测问题。该方法结合系统的约束条件,设置动态的搜索区间,进行优化搜索。非线性动态系统仿真实验表明,在保证较好的稳定性和较强鲁棒性的同时,优化速度加快了。5、提出了一种模糊神经混合系统的建立方法。在反馈学习算法的基础上,将模糊逻辑和神经网络自适应控制的结构结合在一起。实验结果表明,该方法提高了系统对非线性和不确定性特性的处理能力,仿真结果几乎没有超调量,即使在模型失配的情况下仍然能取得满意的控制品质。更多还原

【Abstract】 Fuzzy-neural control is an active research area of intelligent control theory. There are still some theoretical and practical problems although fuzzy-neural nets have been applied in complicated system control and modeling. The objective of this thesis is to address some of these problems, such as the learning ability of fuzzy-neural network, the generation of fuzzy rule, the integration approaches of fuzzy logic and neural network, the application of fuzzy-neural nets to complicated system identification and control.The major contributions of the thesis include:1. A survey on the application of fuzzy-neural network to complicated system identification and control is presented. The problems in its theory and applications are analyzed.2. A study on fuzzy-neural network is conducted according to the problems aforementioned. A new construction method for fuzzy-neural system is proposed based on the analysis and comparison of existing methods, which is able to generate the fuzzy rules automatically.3. A new system identification method based on fuzzy-neural nets is proposed using the new construction method Experimental results show that the proposed method improves the identification accuracy, accelerates the convergence of the back-propagation learning, and enhances the stability of system.4. An adaptive fuzzy-neural network-based generalized predictive control algorithm is developed for nonlinear system. The algorithm can be used to the nonlinear system which is constrained by the range and velocity of control signal. The optimization of the algorithm is based on golden section approach which is non-derivative-based. The search space is dynamically set according to the constraint conditions of the system. The simulation testing shows that the optimization is accelerated while the stability and robustness of system are guaranteed.5. A new construction approach is proposed for fuzzy-neural hybrid system. The neural network-based adaptive control and fuzzy logic are integrated based on feedback learning algorithm. Experimental results show that it is able to deal with the nonlinearity and uncertainty of system. Overshooting is hardly observed in the experiments. Furthermore, it can tolerate the unmatched model to some extent.更多还原