Stable Kalman filter and neural network for the chaotic systems identification

In this research, a modified Kalman filter is introduced for the adaptation of a neural network. The modified Kalman filter is an improved version of the extended Kalman filter based in the following two changes: (1) a term of the weights adaptation is modified in the modified algorithm to assure the uniform stability, convergence of the weights error, and local minimums avoidance, (2) the activation functions are used instead of the Jacobian terms in the modified algorithm to assure the boundedness of the weights error. The suggested algorithm is applied for the chaotic systems identification.