基于混沌PSO算法的选择性神经网络集成方法

提出基于十进制粒子群优化算法（DePSO）和二进制PSO算法（BiPSO）的选择性神经网络集成(NNE)方法，通过PSO算法合理选择组成神经网络集成的各个神经网络，使个体间保持较大的差异度，减小"多维共线性"和样本噪声的影响。为有效保证PSO算法的粒子多样性，在迭代过程中加入混沌变异。试验表明，混沌PSO算法是组合优化权值的有效方法，同已有方法比较可以有效提高神经网络集成的泛化能力。

Selective neural network ensemble methods based on chaos PSO

Selective Neural Network Ensemble (NNE) methods based on Decimal Particle Swarm Optimization (DePSO) and Binary Particle Swarm Optimization (BiPSO) were proposed in this paper. The basic idea of the methods was to optimally select Neural Networks (NNs) to construct NNE with the aid of PSO. This may maintain the diversity of NNs and decrease the effect of collinearity and noise of sample. Meanwhile, chaos mutation was adopted in order to increase the diversity of particles of PSO. The experimental results show that the chaos PSO algorithm is an effective ensemble method, and it may improve the generalization ability of NNE in comparison with the available ones.