Time Series Predication Based on Genetic Chaotic Operators Network
Scientifically prediction of some statistical data in practical production can guide mission planning and scheduling, policy-making and emergency treatment. A new dynamic prediction network is proposed to improve the prediction performance of conventional method. The prediction network is composed of many chaotic operators, and its control parameters are optimized by genetic algorithm. The dynamic characteristic of the network can be changed to follow that of the system predicted. The prediction results of actual data, such as passenger traffic, freight traffic, goods volume, and passenger volume, show that the method is valid, and it has good predictive ability and precision.
Genetic algorithm Time series Chaos Predication
Yu Ting-ting Xiu Chun-bo Liu Yu-xia
Key Laboratory of Advanced Electrical Engineering and Energy Technology, School of Electrical Engine Key Laboratory of Advanced Electrical Engineering and Energy Technology, School of Electrical Engine Department of Mechanical and Electrical Engineering, Shandong Water polytechnic college, Rizhao 2768
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
1118-1121
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）