系统工程理论与实践
繫統工程理論與實踐
계통공정이론여실천
Systems Engineering—Theory & Practice
2013年
1期
243~248
,共null页
龙文 梁昔明 龙祖强 阎纲
龍文 樑昔明 龍祖彊 閻綱
룡문 량석명 룡조강 염강
粒子群优化 灰色预测 最小二乘支持向量机 组合模型 地下水埋深预测
粒子群優化 灰色預測 最小二乘支持嚮量機 組閤模型 地下水埋深預測
입자군우화 회색예측 최소이승지지향량궤 조합모형 지하수매심예측
particle swarm optimization; grey forecasting; least squares support vector machine; combi-national model; prediction of groundwater depth
针对LSSVM参数难以确定和单一方法预测精度不高的问题，提出一种基于粒子群优化LSSVM灰色组合预测模型的学习方法．利用粒子群算法的收敛速度快和全局优化能力，优化LSSVM模型的惩罚因子和核函数参数．避免了人为选择参数的盲目性．在同一时刻利用不同长度序列的灰色预测方法对历史数据进行初步预测，将初步预测结果的组合作为LSSVM的输入，该时刻的实际值作为输出，进行训练建立灰色LSSVM组合预测模型，提高了模型的推广预测能力．选取三江平原某地区1985年至2006年地下水埋深实测数据，建立PSO—LSSVM组合预测模型．通过两种方式对模型进行检验，与其他模型相比，该组合模型具有较高的预测精度．
針對LSSVM參數難以確定和單一方法預測精度不高的問題，提齣一種基于粒子群優化LSSVM灰色組閤預測模型的學習方法．利用粒子群算法的收斂速度快和全跼優化能力，優化LSSVM模型的懲罰因子和覈函數參數．避免瞭人為選擇參數的盲目性．在同一時刻利用不同長度序列的灰色預測方法對歷史數據進行初步預測，將初步預測結果的組閤作為LSSVM的輸入，該時刻的實際值作為輸齣，進行訓練建立灰色LSSVM組閤預測模型，提高瞭模型的推廣預測能力．選取三江平原某地區1985年至2006年地下水埋深實測數據，建立PSO—LSSVM組閤預測模型．通過兩種方式對模型進行檢驗，與其他模型相比，該組閤模型具有較高的預測精度．
침대LSSVM삼수난이학정화단일방법예측정도불고적문제，제출일충기우입자군우화LSSVM회색조합예측모형적학습방법．이용입자군산법적수렴속도쾌화전국우화능력，우화LSSVM모형적징벌인자화핵함수삼수．피면료인위선택삼수적맹목성．재동일시각이용불동장도서렬적회색예측방법대역사수거진행초보예측，장초보예측결과적조합작위LSSVM적수입，해시각적실제치작위수출，진행훈련건립회색LSSVM조합예측모형，제고료모형적추엄예측능력．선취삼강평원모지구1985년지2006년지하수매심실측수거，건립PSO—LSSVM조합예측모형．통과량충방식대모형진행검험，여기타모형상비，해조합모형구유교고적예측정도．
To solve the problems of the uncertain parameters of LSSVM and the low forecasting precision of single method, the learning algorithm of grey least squares support vector machines combined forecasting model optimized by particle swarm algorithm is proposed. Optimize two parameters of LSSVM model study by particle swarm algorithm＇s abilities of the fast convergence and whole optimization. It can escape from the blindness of man-made choice. First, the combinational results of initial forecasts are put as the input and the corresponding actual values are put as the output of LSSVM. Then we can get combinational model of the grey and the least squares support vector machine based on particle swarm algorithm by training it. The proposed combinational model can enhance the etYiciency and the capability of forecasting. Actual data from 1985 to 2006 of area in Sanjiang plain is taken as the sample data. A combinational model based on PSO-LSSVM and GM（1,1） model is proposed. Predict precision of the model is examined by two ways, and the results show that it is more precise than the other methods.
