基于神经网络的多值模糊控制系统研究

对于难以用精确数学模型描述的多变量非线性复杂控制系统，靠传统控制理论难以获得理想的控制效果，基于模糊神经网络控制的技术不依赖于被控对象精确的数学模型，且能根据被控对象参数的变化自适应调节控制规则和隶属函数参数的特性本文介绍了模糊神经网络控制器的应用研究，经过仿真实验证明该控制器能够获得较理想的控制效果。

Fuzzy control system research with many numbers based on neural network

There are some multi-valued control objects with characteristics of nonlinearity, time lag, uncertainty. Traditional control theories do not obtain perfect performance for them. Fuzzy neural controller is a kind of intelligent controller which does not require accurate model of plant and is able to learn to control adaptively. This paper proposes a kind of fuzzy neural network control system based on learning algorithm and multi-valued function which derives from the fusion of the fuzzy logic and neural network. The simulation experiment results show that the performance of the system is improved.