Fuzzy Control And Neural Network Methods

In this paper, the theories of fuzzy logic control and neural networks is introduced. Then, some shortcomings of the existing methods of fuzzy control and neural networks are pointed out and improved upon. Different methods of neural networks and integral neural-fuzzy technique are used in the control of a typical two-order lingering system. The effectiveness of each method is verified through simulation tests. The main contribution of this dissertation is summarized as follows:1.Through improving the choice of learning rate and implementing online adjustment of control plus, a simple and rapid single-nerve adaptive control method with learning ability is proposed, the effectiveness of which is proved by simulation.2.Based on the original BP network, some improvement on error back propagation arithmetic is made. The executing speed of the algorithm is increased through online adjustment of learning rate. Combined with traditional PID control, this method generated two integral schemes: BP network + PID serial control and self-confirming control of parameters of PID controller based on BP network are constructed. Application results in the control of two-order lingering system demonstrate the effectiveness of the two methods.3.Different integral neural-fuzzy techniques are adopted to improve the traditional smith predictive control and model arithmetic predictive control, which results in control systems with strong robustness. (1) The old PID controller of the anterior scheme is substituted by improved PT fuzzy controller with optimal correction function, and the parameters of the segment of P1 are adjusted online by BP neural network. (2) Preserving the intrinsic advantage of the posterior scheme, a predictive fuzzy control. system on neural network is constructed by replacing the old predictive model and PID controller with?.well-trained neural network and the improved fuzzy controller. The effectiveness of the two schemes is verified by the simulation results.