计算机应用
計算機應用
계산궤응용
COMPUTER APPLICATION
2014年
z1期
117-119,155
,共4页
支持向量机%模拟退火%粒子群优化算法%情感识别%生理信号
支持嚮量機%模擬退火%粒子群優化算法%情感識彆%生理信號
지지향량궤%모의퇴화%입자군우화산법%정감식별%생리신호
Support Vector Machine ( SVM)%Simulated Annealing ( SA)%Particle Swarm Optimization ( PSO)%emotion recognition%physiological signal
针对传统支持向量机核函数参数σ、惩罚系数γ以及不敏感损失常数ε需要优化的问题，提出模拟退火免疫粒子群算法( SA-IPSO)优化支持向量机( SVM)关键参数σ、γ、ε的方法。并使用BIOPAC MP150对于630名被试者进行了情感激发状态下的心电生理信号采集，构建可靠的情感生理信号数据库，用该算法对其分类，与模拟退火支持向量机( SA-SVM)以及默认参数支持向量机相比，识别率更高，误报率更低，说明该算法在情感识别领域识别效果优于传统支持向量机。
針對傳統支持嚮量機覈函數參數σ、懲罰繫數γ以及不敏感損失常數ε需要優化的問題，提齣模擬退火免疫粒子群算法( SA-IPSO)優化支持嚮量機( SVM)關鍵參數σ、γ、ε的方法。併使用BIOPAC MP150對于630名被試者進行瞭情感激髮狀態下的心電生理信號採集，構建可靠的情感生理信號數據庫，用該算法對其分類，與模擬退火支持嚮量機( SA-SVM)以及默認參數支持嚮量機相比，識彆率更高，誤報率更低，說明該算法在情感識彆領域識彆效果優于傳統支持嚮量機。
침대전통지지향량궤핵함수삼수σ、징벌계수γ이급불민감손실상수ε수요우화적문제，제출모의퇴화면역입자군산법( SA-IPSO)우화지지향량궤( SVM)관건삼수σ、γ、ε적방법。병사용BIOPAC MP150대우630명피시자진행료정감격발상태하적심전생리신호채집，구건가고적정감생리신호수거고，용해산법대기분류，여모의퇴화지지향량궤( SA-SVM)이급묵인삼수지지향량궤상비，식별솔경고，오보솔경저，설명해산법재정감식별영역식별효과우우전통지지향량궤。
This paper discussed the optimization method about parametersσ,ε, andγfor Support Vector Machine ( SVM) and raised a new SVM algorithm which was improved by the Simulated Annealing-Immune Particle Swarm Optimization ( SA-IPSO) . BIOPAC MP150 was used to test 630 subjects and get their biological signals to build a reliable database. The database was classified by the new algorithm and comparisons were made with SVM and Simulated Annealing-SVM. Results indicate that using this new algorithm, the True Positive Rate ( TPR) is higher and the False Positive Rate ( FPR) is lower, which means that it has better identifying effect in emotion recogniton.
