Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm

Jinna Lu; Hongping Hu; Yanping Bai

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Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm

机译：基于改进的动态粒子群算法和AdaBoost算法的广义径向基函数神经网络

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摘要

This paper proposes an improved dynamic particle swarm optimization algorithm, which uses a new and effective exponential decreasing inertia weight (EDIW) strategy. Based on the improved EDIW-PSO algorithm together with AdaBoost algorithm, we adjust the parameters (centers, widths, shape parameters and connection weights) of GRBF and present a novel hybrid EDIW-PSO-AdaBoost-GRBF model. Two application examples are given on the proposed model. The results obtained show that the proposed model is effective and feasible for prediction problems.

机译：本文提出了一种改进的动态粒子群算法，该算法采用了一种新的有效的指数递减惯性权重（EDIW）策略。基于改进的EDIW-PSO算法和AdaBoost算法，我们调整了GRBF的参数（中心，宽度，形状参数和连接权重），并提出了一种新颖的EDIW-PSO-AdaBoost-GRBF混合模型。在该模型中给出了两个应用示例。所得结果表明，该模型对预测问题是有效可行的。

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来源
《Neurocomputing》 |2015年第25期|305-315|共11页
作者
Jinna Lu; Hongping Hu; Yanping Bai;
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作者单位

School of Information and Communication Engineering, North University of China, Shanxi, Taiyuan 030051, People's Republic of China,Department of Mathematics, North University of China, Shanxi, Taiyuan 030051, People's Republic of China;

Department of Mathematics, North University of China, Shanxi, Taiyuan 030051, People's Republic of China;

Department of Mathematics, North University of China, Shanxi, Taiyuan 030051, People's Republic of China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Generalized radial basis function; Dynamic particle swarm optimization; Exponential decreasing inertia weight; AdaBoost algorithm;

机译：广义径向基函数;动态粒子群优化;指数递减惯性权重;AdaBoost算法;

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Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm

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