计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
12期
1-5
,共5页
粒子群算法%支持向量机%围岩松动圈%仿真预测
粒子群算法%支持嚮量機%圍巖鬆動圈%倣真預測
입자군산법%지지향량궤%위암송동권%방진예측
Particle Swarm Optimization(PSO)%Support Vector Machine(SVM)%loosening zones around roadway%sim-ulation and forecast
针对目前巷道围岩松动圈确定方法的种种缺陷,提出了一种新的预测方法,采用改进的粒子群算法(MPSO)优化支持向量机(SVM)对巷道围岩松动圈进行预测。在标准PSO中引入压缩因子,实现了算法全局搜索和局部寻优的有效平衡;应用MPSO对SVM的参数C和g进行优化,建立MPSO-SVM回归预测模型;将该预测模型应用于巷道围岩松动圈的预测,将预测性能与PSO-SVM、GA(遗传算法)-SVM、GSM(网格搜索)-SVM模型、BP神经网络进行对比分析。结果表明:该模型具有较强的泛化能力,较高的预测精度,可以对围岩松动圈厚度进行有效预测。
針對目前巷道圍巖鬆動圈確定方法的種種缺陷,提齣瞭一種新的預測方法,採用改進的粒子群算法(MPSO)優化支持嚮量機(SVM)對巷道圍巖鬆動圈進行預測。在標準PSO中引入壓縮因子,實現瞭算法全跼搜索和跼部尋優的有效平衡;應用MPSO對SVM的參數C和g進行優化,建立MPSO-SVM迴歸預測模型;將該預測模型應用于巷道圍巖鬆動圈的預測,將預測性能與PSO-SVM、GA(遺傳算法)-SVM、GSM(網格搜索)-SVM模型、BP神經網絡進行對比分析。結果錶明:該模型具有較彊的汎化能力,較高的預測精度,可以對圍巖鬆動圈厚度進行有效預測。
침대목전항도위암송동권학정방법적충충결함,제출료일충신적예측방법,채용개진적입자군산법(MPSO)우화지지향량궤(SVM)대항도위암송동권진행예측。재표준PSO중인입압축인자,실현료산법전국수색화국부심우적유효평형;응용MPSO대SVM적삼수C화g진행우화,건립MPSO-SVM회귀예측모형;장해예측모형응용우항도위암송동권적예측,장예측성능여PSO-SVM、GA(유전산법)-SVM、GSM(망격수색)-SVM모형、BP신경망락진행대비분석。결과표명:해모형구유교강적범화능력,교고적예측정도,가이대위암송동권후도진행유효예측。
A new method using Support Vector Machine(SVM)which is optimized by Modified Particle Swarm Optimi-zation(MPSO)to predict loosening zones around roadway is proposed, avoiding shortcomings of current determining methods. Compression factor is introduced to standard PSO. Effective balance between global search and local optimiza-tion is achieved. MPSO is applied to optimizing the SVM parameters C and g, and MPSO-SVM regression prediction model is established. The model is applied to predicting loosening zones around roadway. The prediction result is com-pared with PSO-SVM, GA(Genetic Algorithm)-SVM, GSM(Grid Search Method)-SVM, BP neural network. The results show that the model has better generalization performance and higher prediction accuracy. It can effectively predict loos-ening zones around roadway.