AN INTELLIGENT CONTROL SYSTEM BASED ONRECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information ρy/ρu for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is im-plemented to construct RNNM on line. Lastly, in order to evaluate the performance of the proposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.     

求解模糊目标／资源问题的拟人化算法

介绍一种求解具有模糊资源约束线性规则问题的拟人化算法,它将模糊控制算法嵌在遗传算法之中可以解出一个模糊优解,取代了以往利用单纯形法求解模糊线性规划得到的唯一最优解,通过例子,可以看出该算法能得到一个满意度很高的解．