The Research and Application of the Fuzzy Neural Network Control Based on Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 191-195
Author(s):  
Yong Chao Zhang ◽  
Wen Zhuang Zhao ◽  
Jin Lian Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Intelligent Control System ◽  
Neural Network Controller ◽  
Network Controller ◽  
Controller Performance ◽  
Fuzzy Neural ◽  
Neural Network Structure

How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network intelligent control system.

Study on the Fuzzy Neural Network Control Used in Wastewater Treatment

2011 ◽  
Vol 71-78 ◽  
pp. 3127-3132 ◽  
Author(s):  
Zhong Qi Wang ◽  
Cheng Zhao
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Dissolved Oxygen ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Neural Network Control ◽  
Neural Network Controller ◽  
Network Controller ◽  
Strong Robustness ◽  
Fuzzy Neural

In this paper, we introduce the study on fuzzy neural network control used in wastewater treatment. An effective fuzzy neural network controller is proposed. The simulation result shows that the system gives strong robustness and good dynamic characteristics. It is used to control dissolved oxygen and forecast water quality. The result indicates that the concentration of dissolved oxygen can reach expectation fleetly and effectively. The model has better precision of forecasting and faster speed of convergence.

Fuzzy Neural Network Control for DC-Motor Micromaneuvering

1997 ◽  
Vol 119 (2) ◽  
pp. 312-315 ◽  
Author(s):  
Anthony Tzes ◽  
Pei-Yuan Peng
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Time Window ◽  
Back Propagation ◽  
Time History ◽  
Dc Motor ◽  
Network Control ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural

The application of a fuzzy neural network controller for compensating the effects induced by the friction in a DC-motor micromaneuvering system is considered in this article. A back-propagation neural network is employed to decrease the effects of the system nonlinearities. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. A fuzzy cell space controller supervises the overall scheme and reduces the amplitude and repetitions of control switchings. Simulation studies are presented to indicate the effectiveness of the proposed algorithm.

Fuzzy Neural Network Controller Based on Improved Genetic Algorithm for Temperature of Glass Tempering and Annealing Process

2010 ◽  
Vol 154-155 ◽  
pp. 214-219
Author(s):  
Xiao Kan Wang ◽  
Zhong Liang Sun ◽  
Sanci Guo ◽  
Chao Qun Shen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Annealing Process ◽  
Time Varying ◽  
Improved Genetic Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Time Varying Parameters ◽  
Fuzzy Neural

The temperature control of the glass tempering and annealing process has characteristics of time-varying parameters，nonlinear and big lag. It is difficult to meet the expected control effect with the common control method. To solve this problem，this paper puts forward a kind of fuzzy neural network controller optimized by genetic algorithm. First，it uses neural network to construct fuzzy logic system according to the structure equivalence rule，thus the optimization of fuzzy control rules and membership functions can be realized by finding the weight value of the neural network. Then，it uses the improved genetic algorithm to find the global optimum weighted factors with a high speed so to improve the performance of the controller. The simulation results show that the optimized fuzzy neural network controller can obtain an excellent control performance for the nonlinearity system with time- varying parameters and lag.

Varying Parameters of the Object Model on Fuzzy Neural Network Controller for Activated Sludge Method of the Sewage Treatment System

2011 ◽  
Vol 230-232 ◽  
pp. 339-345
Author(s):  
Zhao Hui Shi ◽  
Cheng Zhi Wang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Sewage Treatment ◽  
Network Control ◽  
Neural Network Control ◽  
Dissolved Oxygen Concentration ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural

In this paper, we take characteristics of wastewater treatment and process technology, drawing on the effectiveness of thetraditional PID control and on the basis of its lack, with the key steps in the sewage treatment process - Aeration control of part of the process parameters, Fuzzy neural network control of dissolved oxygen concentration (DO) to achieve negative feedback control loop,design a model-based closed-loop cascade control system. Fuzzy systems, membership function, the structure of the network topology and algorithms are based on the actual issues identified in the fuzzy variables. Aiming at the four parts of the fuzzy control, adopting four fuzzy neural network based on the standard model - the input layer, Fuzzy layer,Inference layer,Clear layer are corresponding with it. Standing on two points: the dissolved oxygen concentration control and the rate of change from the error ,then design the Fuzzy neural network controller. Then the fuzzy neural network control technology could be used in wastewater treatment on the specific application of process control.

Fuzzy Neural Network Controller Based on Hybrid GA-BP Algorithm

2013 ◽  
Vol 823 ◽  
pp. 335-339 ◽  
Author(s):  
Yin Ping Chen ◽  
Hong Xia Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Random Search ◽  
Bp Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Initial States ◽  
Searching Ability

This paper presents a hybrid GA-BP algorithm for fuzzy neural network controller (FNNC). BP algorithm is a method to monitor learning, easily realized and with good local searching ability. But it depends too much on the the initial states of the network. Genetic algorithm is a random search algorithm which has strong global searching ability. The hybrid GA-BP algorithm ensure the global convergence of learning by genetic algorithm, overcomes the BP algorithms dependency on the initial states on the one hand. On the other hand, combined with the BP algorithm overcome the simple genetic algorithms randomness, improve the searching efficiency. The simulations on the inverted pendulun problem show good performance and robustness of the proposed fuzzy neural network controller based on hybrid GA-BP algorithm.

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