Particle swarm optimization （PSO） was modified by variation method of particle velocity, and a variation PSO （VPSO） algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and loc...Particle swarm optimization （PSO） was modified by variation method of particle velocity, and a variation PSO （VPSO） algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network （ENN） to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm （GA） and BP neural network （GA-BP）, VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification.展开更多
基金The National Natural Science Foundation of China (No.E50774076)
文摘Particle swarm optimization （PSO） was modified by variation method of particle velocity, and a variation PSO （VPSO） algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network （ENN） to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm （GA） and BP neural network （GA-BP）, VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification.
