摘要
改进的自适应粒子群优化算法根据群体早熟收敛程度和个体适应值来调整惯性权重和阈值系数,以及对粒子速度与位置进行更新,该算法兼顾全局寻优和局部寻优,有效地避免早熟收敛。使用改进的自适应粒子群优化算法训练神经网络,并根据汽车线控转向系统构建故障诊断模型。实验结果表明:与传统的粒子群优化算法、遗传算法训练神经网络相比,基于改进的自适应粒子群优化算法的神经网络能够有效改善神经网络的训练效率,加快了收敛速度,提高故障模式识别的准确率。
A new particle swarm optimization algorithm with dynamically changing the inertial weight and threshold value based on the improved particle swarm optimization is proposed, in which the inertial weight of the particle is adaptively adjusted based on the premature convergence degree of the swarm and the fitness of the particle. The diversity of inertial weight makes a compromise between the global convergence and local convergence, so it can effectively alleviate the problem of premature convergence. The algorithm is applied to train neural network and a model of fault diagnosis for steer-by-wire is established. The simulation results illustrate that compared with particle swarm optimization algorithm and genetic algorithm, the proposed algorithm can effectively improve the training efficiency of neural network, speed up the convergence rate and obtain good diagnosis results.
出处
《传感器与微系统》
CSCD
北大核心
2010年第9期39-41,44,共4页
Transducer and Microsystem Technologies
基金
广西研究生教育创新计划资助项目(2008105940814M03)
关键词
改进的自适应粒子群算法
神经网络
故障诊断
汽车线控转向
improved self-adaptive particle swarm algorithm
neural network
fault diagnosis
steer-by-wire
