| 利用Alopex改进的粒子群优化算法及其在软测量建模中的应用 |
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| 引用本文： | 李绍军,张绪杰,王惠,钱锋.利用Alopex改进的粒子群优化算法及其在软测量建模中的应用[J].医学教育探索,2006(9):1104-1108. |
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| 作者姓名： | 李绍军 张绪杰 王惠 钱锋 |
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| 作者单位： | 华东理工大学自动化研究所 上海200237 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)
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上海市科委资助项目
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| 摘 要： | 提出了一种利用A lopex算法改进的粒子群优化算法,并将其应用于神经网络的建模中。改进的粒子群优化算法改善了粒子群优化算法摆脱局部极小点的能力,对典型函数的测试和基于神经网络的软测量建模表明:改进算法的全局搜索能力有了显著提高,特别是对多峰函数能够有效地避免早熟收敛问题。
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| 关 键 词： | 粒子群优化算法 Alopex 种群多样性 软测量 神经网络 |
| 收稿时间： | 7/1/2005 12:00:00 AM |
Improved Particle Swarm Optimization Algorithms by Alopex and Its Application in Soft Sensor Modeling |
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| LI Shao-jun,ZHANG Xu-jie,WANG Hui,QIAN Feng.Improved Particle Swarm Optimization Algorithms by Alopex and Its Application in Soft Sensor Modeling[J].Researches in Medical Education,2006(9):1104-1108. |
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| Authors: | LI Shao-jun ZHANG Xu-jie WANG Hui QIAN Feng |
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| Abstract: | Particle swarm optimization is a simple stochastic global optimization technique.Its significant feature is simpler expression and less parameters,but it is easily slumped local minima.A particle swarm optimization algorithm improved by Alopex is brought forward.The proposed algorithm sustains diversity in population efficiently and improves the ability of breaking away from local minima.At last the improved algorithm is used to model the soft sensor based on artificial neural networks.The(experiment) results demonstrate that the proposed algorithm is superior to the original particle swarm optimization(algorithm),especially multi-apices function. |
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| Keywords: | particle swarm optimization Alopex population diversity soft sensor neural networks |
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