南京理工大学学报（自然科学版）2024，Vol.48Issue(2)：202-208,226,8.DOI:10.14177/j.cnki.32-1397n.2024.48.02.010
遗传算法与修正的自适应矩估计优化循环神经网络的心音分类方法
Heart sound classification method combining GA with rectified Adam for RNN optimization
摘要
Abstract
Aiming at the problems of gradient explosion,gradient disappearance,and short-term memory in traditional recurrent neural networks(RNN)for identifying and classifying heart sound signals,a heart sound classification model is proposed combining genetic algorithm(GA)and rectified Adam(RAdam)optimized RNN without heart sound segmentation.The advantage of this model is that it integrates GA and RAdam optimizer in series into a RNN to improve its performance.Firstly,the selection,mutation and genetic operation of the GA are used to optimize the number of nodes in the input layer of the RNN,and the initial solution of the optimal individual of the heart sound feature vector is obtained.Secondly,according to the weight and bias matrix in the optimal individual,the initial weight and threshold are assigned to the model,and the optimal solution of the initial weight is obtained,and the entire model shares parameters.Finally,combined with the improved learning rate adaptive optimization algorithm,the RNN model is optimized.The results show that combining the classical Mel-frequency cepstral coefficient method to extract the eigenvectors of the heart sound signal,the classification accuracy of the heart sound signals reaches 90.29％,which is 17.79 percentage points higher than that of the unoptimized RNN model.关键词
遗传算法/自适应矩估计/循环神经网络/心音分类Key words
genetic algorithm/Adam/recurrent neural network/heart sound classification分类
通用工业技术引用本文复制引用
吴全玉,刘美君,范家琪,潘玲佼,陶为戈..遗传算法与修正的自适应矩估计优化循环神经网络的心音分类方法[J].南京理工大学学报（自然科学版）,2024,48(2):202-208,226,8.基金项目
国家自然科学基金(62001196) （62001196）
江苏省重点研发计划(SBE2020648) （SBE2020648）
常州市社会发展项目(CE20225045) （CE20225045）
