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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Fast and Efficient Second-Order Method for Training Radial Basis Function Networks
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Fast and Efficient Second-Order Method for Training Radial Basis Function Networks

机译：训练径向基函数网络的快速高效的二阶方法

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摘要

This paper proposes an improved second order (ISO) algorithm for training radial basis function (RBF) networks. Besides the traditional parameters, including centers, widths and output weights, the input weights on the connections between input layer and hidden layer are also adjusted during the training process. More accurate results can be obtained by increasing variable dimensions. Initial centers are chosen from training patterns and other parameters are generated randomly in limited range. Taking the advantages of fast convergence and powerful search ability of second order algorithms, the proposed ISO algorithm can normally reach smaller training/testing error with much less number of RBF units. During the computation process, quasi Hessian matrix and gradient vector are accumulated as the sum of related sub matrices and vectors, respectively. Only one Jacobian row is stored and used for multiplication, instead of the entire Jacobian matrix storage and multiplication. Memory reduction benefits the computation speed and allows the training of problems with basically unlimited number of patterns. Several practical discrete and continuous classification problems are applied to test the properties of the proposed ISO training algorithm.
机译：本文提出了一种用于训练径向基函数（RBF）网络的改进的二阶（ISO）算法。除了传统的参数（包括中心，宽度和输出权重）外，在训练过程中还会调整输入层和隐藏层之间的连接上的输入权重。通过增加可变尺寸可以获得更准确的结果。从训练模式中选择初始中心，并在有限范围内随机生成其他参数。利用二阶算法收敛速度快和搜索能力强的优点，所提出的ISO算法通常能够以较少的RBF单元达到较小的训练/测试误差。在计算过程中，准Hessian矩阵和梯度向量分别作为相关子矩阵和向量的总和而累积。只有一个雅可比行被存储并用于乘法，而不是整个雅可比矩阵的存储和乘法。减少内存可以提高计算速度，并可以训练模式数量基本不受限制的问题。应用了一些实际的离散和连续分类问题来测试所提出的ISO训练算法的性质。

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