【作者】 于秀丽；

【导师】 沈雪勤；

【作者基本信息】 河北工业大学 ， 计算机及应用， 2003， 硕士

【摘要】 神经网络的学习算法一直是人工神经网络研究和应用领域中的一个重要问题，尤其是对前向神经网络学习算法(设计)的研究。对此，至今没有一个十分理想的解决办法。本论文在参考了大量国内外有关科技文献的基础上，对神经网络学习算法作了深入的研究，给出了切实可行的算法，主要解决了非线性样本的分类问题。 本文由两部分组成：第一部分侧重对径向基神经网络的研究，实现了一种基于RBF网络的新的学习算法；第二部分侧重对前向神经网络的规划学习算法的研究，根据支持向量机理论与神经网络的规划算法的关系，给出了一种新的神经网络基于规划学习算法。 第一部分从RBF网络出发，通过递归分割将输入空间划分为两部分，从而将输入空间变成一个用超矩形构成的回归树(二叉树)。回归树的结点可以很容易的转换为径向基函数，通过对回归树结点的访问，可以选择出使网络达到最优的基函数集，形成最终的网络，此算法可以很好的应用到函数逼近、图像处理等方面。 第二部分从神经网络的几何意义出发，根据支持向量机理论与神经网络规划算法的关系，给出了一种新的构造型学习算法。实验表明该学习算法解决了线性不可分样本的分类问题，而且大大的降低了学习复杂度，并能应用到大样本分类问题中。更多还原

【Abstract】 The learning algorithm of neural network has always been an important problem in both research and application fields of artificial neural networks, especially to the study of the learning (design) of feedforward neural networks. Up to now, there’s no practical way good enough to solve it. In this paper, we profoundly research on the learning algorithm of neural networks after referring to lots of domestic and foreign scientific literature and give a practical classification algorithm under the non-linear separability condition.There are two parts in the contents of this thesis. The first part mainly introduces the study of RBF neural networks, realized a new learning algorithm based on RBF neural networks. The second part mainly introduces the study of feedforward neural networks, and presents a new programming based learning algorithms in neural networks under the equivalent between SVM and programming based learning algorithms.The first part of this thesis describes the theory of RBF neural networks. The input space is thus divided into hyperrectangles organized into a regression tree (binary tree) by recursively partition the input space in two. It’s easy to translate the node of regression tree into radial basis function. After the nodes of regression tree are visited, we can generate a set of radial basis functions from which the final network can be selected. This algorithm fits to the application of function approximation, image procession and so on.The second part introduces the geometrical representation of neural networks and presents a new constructive learning approach based on the relationship between SVM based algorithms and programming based learning algorithms in neural networks. Experimental results show that the new algorithm can solve classification problems of non-linear separability samples. It also can greatly reduce the learning complexity and can be applied to real classification problems with a vast amount of data.更多还原