Research On Compensatory Fuzzy Neural Network Based On Genetic Algorithm And Its Application

The steady-state optimization of complicated industrial process is the effec-tive mean for increasing economic benefit, the ultimate end is made system can run stably at optimal operating condition by the premise of guarantee of process safety production for the best economic benefit. With the modern industrial proc-esses becoming more and more complex, the processes show strong associations, nonlinearity and uncertainty, and it is hard to keep the working condition at the best state. Thus, the steady-state industrial production process is taken as a back-ground. This paper mainly studies on improvement of the learning algorithms of CFNN and optimization of network weights. GA algorithm has been effectively improved in accordance with the deficiencies of optimization ability, and using the improved GA algorithm to parameter estimation and optimization in CFNN, it has achieved good results.First, this paper analyses compensatory fuzzy neural network to perform fuzzy reasoning solves the difficulties in the process of designing fuzzy neural network. Initial network model is established by using fuzzy c-means clustering based on subtraction clustering.In the searching process of simple genetic algorithm, there is easy to get into local optimum, the decoding error in encoding methods, and slow convergence speed etc. The paper uses real coding with simple computation and to save com-puting space greatly. Uses steady reproduction, non-uniformity crossover and mutation, in accordance with the deficiencies of getting in local optimization of conditional AGA, the paper presents a improved AGA, the improved GA algo-rithm prevent premature problem and increase the convergence speed of the algo-rithm. Finally, its effectiveness is verified by simulation Shubert function.A hybrid algorithm based on improved GA and BP algorithm for optimizing and adjusting CFNN weights, it colligate the global convergence of GA and strong ability to search locally of BP algorithm, greatly improved global ap- proximation ability and convergence speed of CFNN. Finally, cumene hydroper-oxide (CHP) decomposition process is used as the control example, using im-proved IAGA-CFNN to build CHP decomposition model and the IAGA to obtain the optimal solution. The simulation results shows that the algorithm is a more feasible and efficient Steady-state optimization method.