Application and Parameter Optimization of Dynamic Fuzzy Neural Network Based on Improved Chaotic Particle Swarm Optimization

KUANG Fang-jun; ZHANG Si-yang; XU Wei-hong

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Application and Parameter Optimization of Dynamic Fuzzy Neural Network Based on Improved Chaotic Particle Swarm Optimization

更新时间：2026-09-04

- Application and Parameter Optimization of Dynamic Fuzzy Neural Network Based on Improved Chaotic Particle Swarm Optimization
- Vol. 32, Issue 1, Pages: 48-53(2015)
- 作者机构：
  
  1. 1 南京理工大学计算机科学与工程学院,江苏,南京,210094
  2. 2 湖南安全技术职业学院机构电气与信息工程系,湖南,长沙,410151
  3. 3 长沙理工大学机构计算机与通信工程学院,湖南,长沙,410114
- 作者简介：
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- Published：2015
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匡芳君, 张思扬, 徐蔚鸿. 改进混沌粒子群的动态模糊神经网络参数优化及应用[J]. 微电子学与计算机, 2015, 32(1): 48-53.

KUANG Fang-jun, ZHANG Si-yang, XU Wei-hong. Application and Parameter Optimization of Dynamic Fuzzy Neural Network Based on Improved Chaotic Particle Swarm Optimization[J]. 2015, 32(1): 48-53.
匡芳君, 张思扬, 徐蔚鸿. 改进混沌粒子群的动态模糊神经网络参数优化及应用[J]. 微电子学与计算机, 2015, 32(1): 48-53. DOI：

KUANG Fang-jun, ZHANG Si-yang, XU Wei-hong. Application and Parameter Optimization of Dynamic Fuzzy Neural Network Based on Improved Chaotic Particle Swarm Optimization[J]. 2015, 32(1): 48-53. DOI：

摘要

动态模糊神经网络(DFNN)的性能和学习的稳定性取决于其预设参数的选择

针对DFNN多参数优化问题

提出了改进混沌粒子群优化算法

并将其应用于DFNN神经网络预设参数寻优

以获取最佳参数组合.实验结果表明

该方法能够快速有效地提取DFNN的最优参数组合

具有精度高、收敛快、迭代次数少等特点;利用改进混沌粒子群的动态模糊神经网络构建煤与瓦斯突出预测模型

具有良好的建模效果和更高的预测精度.

Abstract

The performance and the study stability of dynamic fuzzy neural network (DFNN) depend on its preset parameters selection. To the multi-parameter optimization problem of DFNN

improved chaotic particle swarm optimization (ICPSO) is proposed to find the best combination of the preset parameters of DFNN. The experimental results further demonstrate that the ICPSO provides an effective way to search the best parameters combination of DFNN

and has the better precision

the quicker convergence speed and the fewer iteration steps. DFNN model based on improved chaotic particle swarm optimization is built for coal and gas outbursts

which has good modeling and higher prediction accuracy.

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