非线性系统RBF网在线建模的资源优化网络方法

提出了一种RBF网非线性动态系统在线建模的资源优化网络(RON)方法.RON在资源分配网络的学习过程中引入了滑动窗口和网络结构在线优化的思想,使网络能根据最近一段时间内的误差信息自动实现网络结构优化,从而使RBF网既能在线适应对象的变化,又能使网络规模维持在较小水平,并保证了网络的泛化能力.使用滑动窗口技术使RON对学习参数变化具有较好的鲁棒性,并更易收敛.三个标准例子演示了算法的有效性.

Resource-optimizing Networks for Nonlinear System On-line Modeling Using Radial Basis Function Networks

An on-line radial basis function neural networks modeling method for nonlinear dynamics system,called resource-optimizing network(RON),is presented.RON introduces the ideal of sliding window and on-line structure optimization to the standard learning process of resource-allocating network.According to the newest error information,RON optimizes network structure on-line to adapt to the change of system dynamics,and maintains a com- pact network and satisfactory generalization.With the use of sliding window,RON is robust against the changing of learning parameters,and is easy to converge.Three benchmark examples demonstrate the effectiveness of the method.