基于萤火虫优化的BP神经网络算法研究

为了改进神经网络结构和参数的设置方法,在萤火虫算法和BP神经网络的基础上,提出了一种萤火虫算法优化BP神经网络的算法.该算法利用萤火虫算法得到更优的网络初始权值和阈值,弥补BP神经网络连接权值和阈值选择上的缺陷.将该算法应用到Duffing系统产生的混沌时间序列进行算法的有效性验证,并与BP神经网络进行比较,仿真结果表明该算法具有更高的预测准确性,从而证明该算法在该预测领城的可行性和有效性.

Study on Glowworm Swarm Optimized BP Neural Network Algorithm

In order to improve the method of neural network structure and the parameter setting, an algo- rithm for glowworm swarm optimized BP neural network （GSOBPNN） is proposed based on the glowworm swarm optimization （GSO） and BP neural network （BPNN）. In the algorithm,GSO is used to generate bet- ter network initial weights and thresholds so as to compensate the random defects of thresholds and weights of BPNN. The efficiency of the proposed prediction method is tested by the simulation of the cha- otic time series generated by Dulling system. The simulation results show that the proposed method has higher forecasting accuracy compared with the BPNN,thus to prove its feasibility and effectiveness in the chaotic time series.