小世界递归小波神经网络研究
Small-world recurrent wavelet neural networks

针对储备池的适应性问题，提出了小世界递归小波神经网络。首先基于复杂网络理论构建了具有小世界效应的稀疏储备池结构，代替原来的随机拓扑结构，为避免孤立节点的产生，该结构通过在最近邻耦合网络中随机加边来实现。其次，引入了具有良好时频局部特性的小波神经元，包括 Morlet小波、Mexican hat小波、Gaussian小波和B spline小波，并与传统的Sigmoid神经元结合，建立了储备池神经元的混合激励模式。最后，实验仿真结果表明：对比传统的小世界回声状态网络，该模型能够有效地提高对非线性系统的逼近能力。
Small world recurrent wavelet neural networks are proposed for solving the problems about reservoir adaptation.A sparse reservoir with small world feature is built instead of the original random one,and generated from the nearest neighbor coupled network by randomly adding edges to avoid isolated nodes.Then,wavelet neurons are introduced,considering Morlet wavelet,Mexican hat wavelet,Gaussian wavelet,and B spline wavelet.Combined with Sigmoid neurons,a hybrid activation scheme is used for the reservoir.Experimental results show that the model can achieve a more significant enhancement in nonlinear approximation capacity compared with traditional small world echo state networks