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| 基于粒子群神经网络的发动机故障诊断 | |
| 引用本文： | 柴长松,张欣,牛奔,谭立静.基于粒子群神经网络的发动机故障诊断[J].微计算机信息,2007,23(22):186-187,194. |
| 作者姓名： | 柴长松 张欣 牛奔 谭立静 |
| 作者单位： | 1. 100044,北京,北京交通大学机电学院;300160,天津,中国汽车技术研究中心 2. 北京交通大学机电学院,北京,100044 3. 沈阳自动化研究所二室,辽宁沈阳,110016 4. 辽宁工程技术大学机械学院,辽宁阜新,123000 |
| 摘 要： | 本文提出了一种基于粒子群优化算法和多层前向神经网络的新型进化神经网络模型（PSO-ANN）。在实例研究中，该模型被应用于汽车发动机故障诊断中。与传统BP网络诊断结果相比，本文方法具有运算效率高，收敛速度快的特点，是一种有效可靠的新型故障诊断技术。 |
| 关 键 词： | 粒子群 神经网络 汽车发动机 故障诊断 |
| 文章编号： | 1008-0570（2007）08-1-0186-02 |
| 修稿时间： | 2007-05-132007-06-15 |
Automotive Engine Fault Diagnosis Based on PSO Neural Network |
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| CHAI CHANGSONG,ZHANG XIN,NIU BEN,TAN LIJING.Automotive Engine Fault Diagnosis Based on PSO Neural Network[J].Control & Automation,2007,23(22):186-187,194. | |
| Authors: | CHAI CHANGSONG ZHANG XIN NIU BEN TAN LIJING |
| Abstract: | Based on particle swarm optimization and multi-layer feed-forward neural network, a novel evolutionary neural networks model is presented in this paper. In the real-world case study, the proposed model PSO-ANN is applied on the automotive engine fault diagnosis. Compared with the results obtained by BP-ANN, our method has more fast convergence rate and high computation efficiency. It is an efficient and reliable novel fault diagnosis technology. |
| Keywords: | Particle Swarm Neural networks Automotive engine Fault Diagnosis |
| 本文献已被 维普 万方数据 等数据库收录！ | |
