The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-line...The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.展开更多
A mathematical model is developed for an industrial acrylonitrile fluidized-bed reactor based on arti-ficial neural networks. A new algorithm, which combines the characteristics of both genetic algorithm (GA) andgener...A mathematical model is developed for an industrial acrylonitrile fluidized-bed reactor based on arti-ficial neural networks. A new algorithm, which combines the characteristics of both genetic algorithm (GA) andgeneralized delta-rule (GDR) is used to train artificial neural network (ANN) in order to avoid search terminatedat a local optimal solution. For searching the global optimum, a new algorithm called SM-GA, incorporating ad-vantages of both simplex method (SM)and GA, is proposed and applied to optimize the operating conditions of anacrylonitrile fluidized-bed reactor in industry.展开更多
基金This manuscript is supported by the National Natural Science Foundation of China(No.42174011,41874001 and 42174011).
文摘The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.
文摘A mathematical model is developed for an industrial acrylonitrile fluidized-bed reactor based on arti-ficial neural networks. A new algorithm, which combines the characteristics of both genetic algorithm (GA) andgeneralized delta-rule (GDR) is used to train artificial neural network (ANN) in order to avoid search terminatedat a local optimal solution. For searching the global optimum, a new algorithm called SM-GA, incorporating ad-vantages of both simplex method (SM)and GA, is proposed and applied to optimize the operating conditions of anacrylonitrile fluidized-bed reactor in industry.
