计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
8期
160-163
,共4页
混沌时间序列%反向传播(BP)神经网络%差分进化%预测
混沌時間序列%反嚮傳播(BP)神經網絡%差分進化%預測
혼돈시간서렬%반향전파(BP)신경망락%차분진화%예측
chaotic time series%Back Propagation(BP)neural%Differential Evolution(DE)%prediction
针对BP神经网络预测模型收敛速度慢和容易陷入局部极小值的缺点,将差分进化算法和神经网络结合起来,提出了一种基于差分进化算法的BP神经网络预测混沌时间序列的方法,利用差分进化算法的全局寻优能力对BP神经网络的权值和阈值进行优化,然后训练BP神经网络预测模型求得最优解,将该预测方法用到3个典型的混沌时间序列进行算法的有效性验证,并与BP算法的预测精度进行了比较,仿真结果表明该方法对混沌时间序列预测具有更好的非线性拟合能力和更高的预测准确性.
針對BP神經網絡預測模型收斂速度慢和容易陷入跼部極小值的缺點,將差分進化算法和神經網絡結閤起來,提齣瞭一種基于差分進化算法的BP神經網絡預測混沌時間序列的方法,利用差分進化算法的全跼尋優能力對BP神經網絡的權值和閾值進行優化,然後訓練BP神經網絡預測模型求得最優解,將該預測方法用到3箇典型的混沌時間序列進行算法的有效性驗證,併與BP算法的預測精度進行瞭比較,倣真結果錶明該方法對混沌時間序列預測具有更好的非線性擬閤能力和更高的預測準確性.
침대BP신경망락예측모형수렴속도만화용역함입국부겁소치적결점,장차분진화산법화신경망락결합기래,제출료일충기우차분진화산법적BP신경망락예측혼돈시간서렬적방법,이용차분진화산법적전국심우능력대BP신경망락적권치화역치진행우화,연후훈련BP신경망락예측모형구득최우해,장해예측방법용도3개전형적혼돈시간서렬진행산법적유효성험증,병여BP산법적예측정도진행료비교,방진결과표명해방법대혼돈시간서렬예측구유경호적비선성의합능력화경고적예측준학성.
A prediction method for chaotic time series of BP neural based on DE is proposed to overcome the problems such as long computing time and easy to fall into local minimum by incorporating Differential Evolution(DE)and neural network. DE is used to optimize the weights and thresholds of BP neural network, and the BP neural network is used to search for the optimal solution. The efficiency of the proposed prediction method is tested by the simulation of three typical nonlinear systems, and the precision of this algorithm is compared with BP algorithms. The simulation results show that the proposed method has better nonlinear fitting ability and higher forecasting accuracy.
