模糊粗糙神经网络的结构与参数优化

Structure and parameters optimization of fuzzy rough neural network

基于模糊粗糙隶属函数,建立了一种五层结构的模糊粗糙神经网络(fuzzy rough neural network,FRNN),对神经元之间的连接,引入一个开关函数,从而把结构优化和参数学习问题转化为单纯的函数优化问题。提出一种混合智能优化算法(hybrid intelligent optimization algorithm,HIOA)用于FRNN的结构和参数优化,适应度函数同时考虑模型的精确性和网络的节俭性。典型的实验结果表明,FRNN适用非线性系统建模,相对于普通神经网络及其优化方法能获得更高的精度和泛化能力。

A kind of fuzzy rough neural network （FRNN） with five layers is proposed based on fuzzy rough membership functions. A switch function is used to describe the link among neurons, thus both the optimization of structure and parameters learning are transformed to a pure function optimization problem. A hybrid intelli- gent optimization algorithm （HIOA） is proposed to optimize the structure and parameters of FRNN and the fitness function considers both accuracy of the model and succinctness of FRNN simultaneousy. The typical experiment results show that FRNN is suitable for modeling nonlinear systems and it is better than the traditional neural network and its optimization method on both accuracy and generalization.