【摘要】 对于具有非线性、大时滞、不确定性等特性的难以用精确数学模型描述的多变量复杂系统,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,进行了采用模糊神经网络控制器实现其控制的应用研究。采用典型的前向型模糊神经网络模型,给出了具有学习功能的多值模糊神经网络控制系统的一种设计方法。仿真实验证明,该系统能够获得较理想的控制效果。更多还原

【Abstract】 There are some multi-valued processes with characteristics of nonlinearity, time delays and uncertainty. Traditional control theories do not have good performance for them. Fuzzy neural controller is a kind of intelligent controller which does not require accurate model of the plant and is able to learn to control adaptively, A kind of fuzzy neural network control system based on learning algorithm and multi-valued function is proposed which is derived from the combination of fuzzy logic and neural networks. The simulation results show that the performance of the system is improved.更多还原