A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction

Min Gan; Hui Peng; Xue-ping Dong

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A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction

机译：一种优化RBF网络架构和参数的混合算法，用于非线性时间序列预测

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摘要

This paper proposes a novel hybrid algorithm for automatic selection of the proper input variables, the number of hidden nodes of the radial basis function (RBF) network, and optimizing network parameters (weights, centers and widths) simultaneously. In the proposed algorithm, the inputs and the number of hidden nodes of the RBF network are represented by binary-coded strings and evolved by a genetic algorithm (GA). Simultaneously, for each chromosome with fixed inputs and number of hidden nodes, the corresponding parameters of the network are real-coded and optimized by a gradient-based fast-converging parameter estimation method. Performance of the presented hybrid approach is evaluated by several benchmark time series modeling and prediction problems. Experimental results show that the proposed approach produces parsimonious RBF networks, and obtains better modeling accuracy than some other algorithms.

机译：本文提出了一种新颖的混合算法，用于自动选择合适的输入变量，径向基函数（RBF）网络的隐藏节点数，并同时优化网络参数（权重，中心和宽度）。在提出的算法中，RBF网络的输入和隐藏节点的数量用二进制编码的字符串表示，并通过遗传算法（GA）进行进化。同时，对于具有固定输入和隐藏节点数的每个染色体，通过基于梯度的快速收敛参数估计方法对网络的相应参数进行实数编码和优化。通过几种基准时间序列建模和预测问题评估了提出的混合方法的性能。实验结果表明，该方法产生了简约的RBF网络，并且比其他算法具有更好的建模精度。

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来源
《Applied Mathematical Modelling》 |2012年第7期|p.2911-2919|共9页
作者
Min Gan; Hui Peng; Xue-ping Dong;
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作者单位

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;

School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China;

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, 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
radial basis function network; hybrid training approach; time series modeling;

机译：径向基函数网络混合训练方法;时间序列建模;

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机译：使用RBF神经网络和非线性时变演化PSO算法进行时间序列预测
2. Forecasting of Chaotic Time Series Using RBF Neural Networks Optimized By Genetic Algorithms [J] . Awad Mohammed The international arab journal of information technology . 2017,第6期

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3. Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning [J] . Chen S. Electronics Letters . 1995,第2期

机译：使用增强聚类和RLS学习的高斯RBF网络进行非线性时间序列建模和预测
4. RBF NETWORK BASED ON GENETIC ALGORITHM OPTIMIZATION FOR NONLINEAR TIME SERIES PREDICTION [C] . QingNian Zhang, XiangYang He, JianQi Liu IEEE International Symposium on Circuits and Systems . 2003

机译：基于非线性时间序列预测遗传算法优化的RBF网络
5. A hybrid evolutionary algorithm to train neural networks as time-series predictors. [D] . Gallant, Peter Joseph. 2001

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6. Chaos Time Series Prediction Based on Membrane Optimization Algorithms [O] . Meng Li, Liangzhong Yi, Zheng Pei, 2015

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7. A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction [O] . Gan Min, Peng Hui, Dong Xue-ping 2012

机译：一种优化RBF网络架构和参数的混合算法，用于非线性时间序列预测

A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction

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