With the advent of the era of big data,ANNS have been well developed and applied at present.However,in appli-cation,if the fixed learning rate is too large or small,it will face the problem of slow convergence and even divergence.Therefore,in order to avoid the influence of empirical factors on the traditional neural network,the paper introduces an improved method of adaptive learning rate revision function on the basis of adjusting the weight,so as to improve the training speed and stability.Taking the wavelet neural network as an example,it is applied to the trend prediction problem in intelligent operation and maintenance for simulation testing.The results show that compared with the fixed learning rate,the improved algorithm,which is based on a learn-ing rate that can be automatically adjusted,can effectively improve the convergence speed and reduce its convergence errors.
关键词
小波神经网络/自适应学习率/智能运维
Key words
wavelet neural network/adaptive learning rate/intelligent operation and maintenance
维普
万方数据