基于HBF神经网络的自适应观测器

传统的RBF(Radial Basis Function)神经元基函数通常把高斯类型与单一宽度作为每个神经元的激活函数,这些特性限制了网络神经元的性能,特别是在处理复杂的非线性建模问题上.为了克服这个限制,本文应用了具有类似RBF网络,但激活函数不同-超基函数HBF(Hyper Basis Function)的网络.结合RBF网络,分析了HBF网络的结构、基函数形式及基函数对网络的影响,利用决策树算法计算了网络中心.在此基础上,提出了一种基于HBF神经网络的自适应观测器设计方法,并通过引入Lyapunov函数,证明了这种观测器设计方法的稳定性;最后通过仿真验证了这种HBF神经网络观测器能很好地观测系统的状态值.

Adaptive Observer Based on HBF Neural Networks

Conventional RBF neuron network is usually based on Gaussian activation function with single width for each activation function.This feature restricts neuron performance for modeling the complex nonlinear problems.To accommodate limitation of a single scale,this paper applies neural network with similar but yet different activation function-HBF(Hyper Basis Function).The state for nonlinear systems is estimated by using HBF neural networks.Combined with RBF (Radial Basis Function) networks,the structure of networks,the form of its basis functions and its influence on HBF(Hyper Basis Function) are analyzed.Decision tree algorithm is used to determine the network center.Then a design method of adaptive observer based on HBF neural networks is proposed.The Lyapunov function is introduced to prove the stability and the conditions of the bounded error of the observer.And this HBF neural network is turned out to observe system state very well by simulation.