电脑知识与技术
電腦知識與技術
전뇌지식여기술
COMPUTER KNOWLEDGE AND TECHNOLOGY
2013年
4期
861-865
,共5页
主成分分析%混合蛙跳算法%BP神经网络%帕金森氏病%分类
主成分分析%混閤蛙跳算法%BP神經網絡%帕金森氏病%分類
주성분분석%혼합와도산법%BP신경망락%파금삼씨병%분류
principal component analysis (PCA)%shuffle frog leaping algorithm (SFLA)%BP neural network%Parkinson%classifica?tion
针对BP神经网络学习效率低、容易陷入局部最优等缺点,提出了一种基于主成分分析的混合蛙跳算法(Shuffle Frog Leaping Algorithm)优化的BP神经网络模型.使用主成分分析法对高维数据进行特征提取,作为网络输入;采用混合蛙跳算法优化BP神经网络的权系数和阈值,构建基于混合蛙跳算法神经网络的帕金森病分类模型.最后,以UCI中Parkinson数据为例,实验表明,新模型优于传统的BP网络.
針對BP神經網絡學習效率低、容易陷入跼部最優等缺點,提齣瞭一種基于主成分分析的混閤蛙跳算法(Shuffle Frog Leaping Algorithm)優化的BP神經網絡模型.使用主成分分析法對高維數據進行特徵提取,作為網絡輸入;採用混閤蛙跳算法優化BP神經網絡的權繫數和閾值,構建基于混閤蛙跳算法神經網絡的帕金森病分類模型.最後,以UCI中Parkinson數據為例,實驗錶明,新模型優于傳統的BP網絡.
침대BP신경망락학습효솔저、용역함입국부최우등결점,제출료일충기우주성분분석적혼합와도산법(Shuffle Frog Leaping Algorithm)우화적BP신경망락모형.사용주성분분석법대고유수거진행특정제취,작위망락수입;채용혼합와도산법우화BP신경망락적권계수화역치,구건기우혼합와도산법신경망락적파금삼병분류모형.최후,이UCI중Parkinson수거위례,실험표명,신모형우우전통적BP망락.
For the shortcomings of BP neural network which is low learning efficiency and is easy to trap into local optimum, ac?cording to these problems, a new BP neural network model optimized by Shuffle Frog Leaping Algorithm based on Principal Component Analysis is proposed. Using Principle Component Analysis to extract the features of high dimensional data, the input variables;the bias of BP neural network are optimized by Shuffle Frog Leaping Algorithm and then build the classification model of Parkinson's disease based on SFLABP neural network. At last, taking the data of Parkinson from UCI for example, the experi?ment result demonstrates the new model is better than the traditional BP neural network.
