A Modified LOLIMOT Algorithm for Nonlinear Estimation Fusion

In this paper, first an enhanced NeuroFuzzy method for modeling nonlinear system is presented. In this method we use EM algorithm for identification of local models, which gain us model mismatch covariance. The achieved model can be stated in state space model as a linear time-varying system. As the noise and model mismatch covariace is known, Kalman filter can be easily used for centralized estimation fusion. The simulations show that using data fusion will enhance the estimation accuracy to a great deal also accuracy of centralized estimation fusion is better than distributed estimation fusion.