Study On The Normal Fuzzy Neural Network And Application In Intelligent Control Model Of Vanadium Refining

Professor Austrom points out that fuzzy logic control, neural networks and specialist system are three typical intelligent control methods. Specialist system faces many problems and difficulties, such as knowledge acquirement depends on manual transplantation which leads to high cost and low efficiency; applying general clarified set brings forward with feeble reasoning ability, matching collision and combination explosion. Nowadays modern intelligent control focuses on fuzzy logic and neural networks or the combination of the two. Especially the combination of fuzzy logic and neural networks has already become the hotspot in intelligent control research because it absorbs the strongpoint while overcomes the weak point of both.This paper aims at the two main limitations of pure fuzzy system in traditional modeling. One is the difficulties in extracting and adjusting the fuzzy rules and subject functions, the other is the workload increasing exponentially along with the number of variables. So we improve the old regular combination arithmetic on the base of normal fuzzy neural networks (NFNN). Through the arithmetic, we can assemble and combine fuzzy rules gained by neural networks automatically. Furthermore we test its effectiveness by functional simulation experiment. Converter steel-making is a complicated diverse high temperature reaction process and the foundation of the model also is a diverse non-linear mapped process. So it is emergent to find a more superior control model abandoned the tradition static model in the condition that many medium and small converter cannot use an assistant gun. Moreover, we apply NFNN in the problems of static controlling modeling of steel-making and abstract vanadium by converter and come up with the refrigerant adding sub-model. The simulated result accounts for the usefulness and practicability of the modeling method and the regular combination once again.