Novel Real-time Movements Tracking Algorithm Based on Fuzzy Prediction
Wang Jiang-tao · Jisuanji fangzhen · 2011
In order to overcome the difficulty in designing fuzzy rule bases automatically,a novel method using observed data to make the centers of fuzzy bases was proposed,and it was used to predict moving process well and truly.Above of all,only a few data of object states and error of limited measurement were used to construct fuzzy bases at different time directly in the method.Then,the membership value of current state was evaluated according to maximum entropy principle(MEP).In order to make the method fit for different complex movements,Particle Swarm Optimization(PSO) algorithm was adopted to optimize the pivotal parameter of maximum entropy principle in setting generation on the condition of requirement of real time.Moreover,in order to improve the precise of prediction,weighted factor was designed in fuzzy inference processing,and the state and error in next time were calculated according to it,in addition,the state in next time was modified by prediction error.So,prediction error was only determined by limited times measured data,and accumulating error was eliminated.Two different complex movements were used in experiments comparing with method in the other reference,the results show that the method is independent of prior knowledge,and real-time performance and prediction precise are improved.