基于整体退火遗传小波网络的计量终端可靠性预测  

Reliability prediction of metering terminal based on whole annealing genetic algorithm wavelet neural network

作　　者：徐宏伟 丛中笑 阳晓路 周忠明 陈寅生 林海军[2] XU Hongwei;CONG Zhongxiao;YANG Xiaolu;ZHOU Zhongming;CHEN Yinsheng;LIN Haijun(Metrology Center of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China;Harbin University of Science and Technology,Harbin 150080,China)

机构地区：[1]贵州电网有限责任公司计量中心,贵阳550002 [2]哈尔滨理工大学,哈尔滨150080

出　　处：《电测与仪表》2024年第2期179-184,共6页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(61803128)。

摘　　要：为了解决小波神经网络初值敏感性及收敛稳定性问题,以提高计量终端软件可靠性预测建模的效率及准确性。文章完善了整体退火遗传算法(WAGA),并验证了其具有极强的整体收敛和全局优化能力,利用其全局寻优能力,优化小波神经网络(WNN)的参数,提出基于整体退火遗传小波神经网络(WAGA-WNN)的建模方法;用该方法建立计量终端的软件可靠性预测模型。实验结果表明,该方法可以解决小波神经网络初值敏感性及收敛稳定性难题,建立的软件可靠性预测模型效率和准确度较高。In order to solve the problem of initial value sensitivity and convergence stability for the wavelet neural network and improve the efficiency and accuracy of the reliability predictive model for metering terminal software,the following steps are performed.The paper improves the whole annealing genetic algorithm(WAGA),and prove that it has extremely strong ability in global convergence and global optimization.Made use of its global optimization property to improve the parameters for wavelet neural network(WNN)and develop model-building method based on whole annealing genetic algorithm-wavelet neural network(WAGA-WNN).Build software reliability predictive model for metering terminal based on the proposed method.The experimental result indicates that this method can solve the problem of initial value sensitivity and convergence stability for wavelet neural network,furthermore,the software reliability predictive model has high efficiency and accuracy.

关 键 词：整体退火遗传算法 小波神经网络 计量终端 软件可靠性 预测模型 

分 类 号：TM93[电气工程—电力电子与电力传动]