【作者】 南东；

【导师】 吴微；

【作者基本信息】 大连理工大学 ， 计算数学， 2007， 博士

【摘要】 神经网络的理论和方法在过去的十几年发展极为迅速，它的应用范围涉及到工程、计算机、物理、生物、经济、管理等科学领域．人们应用神经网络进行聚类分析、智能控制、模式识别和优化计算等等．然而，许多问题的研究都要转化为用迭代的神经网络逼近函数的问题．该问题在数学上可以解释成，用一元函数的复合来表示多元函数，这也是希尔伯特的第十三个猜想．本文主要基于神经网络的非线性逼近性质，来研究径向基函数神经网络的逼近能力问题，包括函数逼近问题、强逼近问题以及算子逼近问题．即：函数集合在C（K）（或L^p（K））中的稠密问题，在C（K）（或L^p（K））中紧集上的稠密问题以及算子空间T∶L^p1（K₁）→L^p2（K₂）的逼近问题．这里c_i，λ_i∈R_i，x，y_i∈Rⁿ，i=1，2，…，N，K，K₁，K₂（?）Rⁿ为任意紧集，1≤p，p₁，p₂＜∞，激活函数g常常取作高斯函数．函数集合F₁又常常称作是RBF神经网络的数学表达形式．同时本文也研究了一般前馈网络对于完备线性距离空间中紧集上的函数逼近能力，即：如果H是由以‖·‖_H为范数的所有函数构成的完备线性距离空间，V（?）H为一个紧集，函数族在V中稠密问题．这里（?）λ_jg（τ_j（x））是输入x的输出，λ_j是第j个隐单元到输出单元的权值，g是激活函数．τ_j（x）是第j个隐单元的输入值，它是由输入层以及输入层到第j个隐单元之间的权值决定的．根据不同类型的前馈网络，τ_j（x）具有不同的数学表达形式．本论文的结构安排如下：第一章回顾一些有关神经网络的背景知识，其中包括近十几年的前馈神经网络逼近结果．第二章介绍本文中需要的泛函分析和广义函数的基础知识，例如：基本函数空间和广义函数空间的关系，基本函数的支集和广义函数的支撑，以及基本函数和广义函数的卷积，等等．第三章主要讨论径向基神经网络的逼近问题，包括一般函数逼近问题，强逼近问题和算子逼近问题．这些结果推广了径向基网络的逼近结论[1-5]，为RBF神经网络逼近能力的研究提供了有利的理论基础．第四章研究一般前馈神经网络的强逼近问题，并给出了前馈网络的具体形式的强逼近结果：例如MLP网络等等．指出对于带有一个隐层的前馈神经网络，可以预先给定隐单元的个数和输入单元到隐单元的权值，只需选择适当的隐单元到输出单元的权值，就可以对一族函数中的任意函数作逼近．更多还原

【Abstract】 Neural network theory and methods have been developed rapidly in the past two decades and have been applied in diverse areas, such as engineering, computer science, physics, biology, economy and managements, etc. Many researches in this respect can be converted into problems of approximating multivariate functions by superpositions of the neuron activation function of the network. In mathematical terminology, these problems can be expressed that under what conditions can multivariate functions be represented by superposition of univariate functions, which is also the thirteenth conjecture of Hilbert’s.In this thesis, the nonlinear approximation property of neural networks with one hidden layer is investigated and the approximation capability of radial-basis-function （RBF） neural networks is analyzed theoretically, including approximation any given function, approximation a compact set of functions and the system identification capability of RBF neural networks. In other words, under what conditions can the family of functions approximates any given function, a compact set of functions and any given operate T : L^P1（K₁1）→L^P2（K₂）, where c_i,λ_i∈R_i, x, y_i∈Rⁿ, i = 1,2,...,N, K, K₁, K₂（?）Rⁿ are compact sets, 1≤p, P₁, P₂＜∞, the activation function g is typically the Gaussian function, and the network is said to be radial basis function neural network.Moveover, the approximation capability of feedforward neural networks to a compact set of functions is concerned in this thesis. We use to denote a family of neural networks, where F₂（x） is the output of the network for the input x,λ_j the weight between the output neuron and the j-th hidden neuron, and 9 the activation function.τ_j （x） is the input value to the j-th hidden neuron which is determined by the weights between the j-th hidden neuron and the input neurons. To elaborate, we shall prove the following: If a family of feedforward neural networks with a hidden layer is dense in H, a metric linear space of functions, then given a compact set V（?） H and an error boundε, one can choose and fix the quantity of the hidden neurons and the weights between the input and hidden layers, such that in order to approximate any function f∈V with accuracyε, one only has to further choose suitable weights between the hidden and output layers.This thesis is organized as follows:Some background information about FNN is reviewed and some popular results are introduced in Chapter 1.Some elementary sentences and fundamental properties of distributions are introduced in Chapter 2, including the relationship between fundamental space and distributions, supports of distributions, distributions as derivatives, convolutions and so on.The third chapter mainly deals with the approximation capability of RBF neural networks, including approximation any given function, a compact set of functions and any given operate. These result improve some recent results such as [1-5] et. al.The approximation capability of feedforward neural networks to a compact set of functions is investigated in Chapter 4. We follow a general approach that covers all the existing results and gives some new results in this respect. A few examples of straightforward applications of this result to RBF, MLP and other neural networks in some metric linear spaces such as L^p（K） and C（K） is presented in the following. Some of these results have been proved （cf. [1, 2, 6, 7]） in terms of the particular settings of the problems, while the others are new up to this knowledge.更多还原