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计算机系统应用:2020,29(1):244-249
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粒子群退火算法优化的BP神经网络及其应用
(1.太原科技大学 计算机科学与技术学院, 太原 030024;2.中国科学院 地理科学与资源研究所, 北京 100101)
BP Neural Network Optimized by Particle Swarm Annealing Algorithms and Its Application
(1.School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;2.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)
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投稿时间:2019-06-15    修订日期:2019-07-05
中文摘要: 以提高预测软件老化趋势为应用背景,提出一种新型粒子群退火算法(New Particle Swarm Annealing Algorithm,NPSOSA)优化BP神经网络的权值和阈值,继而构建NPSOSA-BP神经网络预测模型.实验通过搭建软件老化测试平台,收集所需的老化数据并进行仿真训练.实验结果表明,NPSOSA-BP神经网络模型相比于传统粒子群算法(PSO)、传统粒子群退火算法(PSOSA)优化的BP神经网络模型提高了预测精度和适用度,在该应用领域验证了本文方法的有效性.
Abstract:In order to improve the forecasting software aging trend, a New Particle Swarm Optimization Simulated Annealing algorithm (NPSOSA) is proposed to optimize the weights and thresholds of BP neural network, and then NPSOSA-BP neural network forecasting model is constructed. The software aging test platform was built to collect the required aging data and conduct simulation training. The experimental results show that the NPSOSA-BP neural network model improves the prediction accuracy and applicability compared with the BP neural network model optimized by the traditional Particle Swarm Optimization (PSO) and the traditional Particle Swarm Optimization Simulated Annealing algorithm (PSOSA). The validity of this method is verified in this application field.
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基金项目:山西省应用基础研究项目(201801D221179);太原科技大学校博士科研启动基金(20162036);教育部产学协同项目(201801128011);全国高等学校计算机教育研究会2019年度课题(CERACU2019R02);太原科技大学教学改革创新项目(201937)
引用文本:
王荣,白尚旺,党伟超,潘理虎.粒子群退火算法优化的BP神经网络及其应用.计算机系统应用,2020,29(1):244-249
WANG Rong,BAI Shang-Wang,DANG Wei-Chao,PAN Li-Hu.BP Neural Network Optimized by Particle Swarm Annealing Algorithms and Its Application.COMPUTER SYSTEMS APPLICATIONS,2020,29(1):244-249

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