BP neural network based on improved seagull optimization algorithm and its application
Aiming at the problem that the initial weights and threshold randomly generated by traditional back propagation(BP )neural network affect the accuracy of prediction,an improved seagull optimization algorithm (ISOA)is proposed for optimizing of initial thresholds and weights of the BP neural netwoek. Firstly,to improve the convergence precision of seagull optimization algorithm(SOA)and the ability to jump out of local optimum,the population is initialized using Sine chaotic mapping,the nonlinear parameter A is introduced,the multiplication and division strategy is introduced to disturb the seagull attack,and the reverse learning strategy is introduced after the attack phase. Then,the ISOA is used to optimize the initial weights and thresholds of the BP neural netwoek to solve the problem of sensitivity to the initial values and easy to fall into the local optimum. Finally,peak stress prediction is carried out in the dynamic impact test of frozen fractured sandstone. The results show that compared with original BP,PSO-BP and SOA-BP,the BP NN optimized by ISOA has higher precision in peak stress prediction.
BP neural networkseagull optimization algorithm (SOA )chaotic mappingmultiplication and division strategyreverse individuals
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维普
