出版社：Canadian Research & Development Center of Sciences and Cultures
摘要：This paper studies the Adaptive neural fuzzy inference systems (ANFIS) based on the fuzzy model Sugeno, on the basis of the network is given the right to amend the value of the algorithm, that is the most comprehensive steep decline in law and the least-squares method to be a mixed learning algorithm. And this paper gives its improved optimization, using conjugate gradient method to improve its premise parameters of learning speed. The concentration of air pollutants has strong nonlinear characteristics, we should conduct more accurate forecasts, it must take to capture nonlinear changes of the forecasting methods. The simulation presented a choice of the most relevant input technology, and achieved very good results. Key words: Sugeno fuzzy model, ANFIS, The conjugate gradient method, Data fitting
其他摘要：This paper studies the Adaptive neural fuzzy inference systems (ANFIS) based on the fuzzy model Sugeno, on the basis of the network is given the right to amend the value of the algorithm, that is the most comprehensive steep decline in law and the least-squares method to be a mixed learning algorithm. And this paper gives its improved optimization, using conjugate gradient method to improve its premise parameters of learning speed. The concentration of air pollutants has strong nonlinear characteristics, we should conduct more accurate forecasts, it must take to capture nonlinear changes of the forecasting methods. The simulation presented a choice of the most relevant input technology, and achieved very good results. Key words: Sugeno fuzzy model, ANFIS, The conjugate gradient method, Data fitting
关键词：Sugeno fuzzy model; ANFIS; The conjugate gradient method; Data fitting
