This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational inference (VI) approach, principled method that has deep connections to maximum likelihood estimation. VI approach ultimately provides estimates of the model as solutions optimisation problem, deterministic, tractable and...

In case of decision making problems, identification nonlinear systems is an important issue. Identification using a multilayer perceptron (MLP) trained with back propagation becomes much complex increase in number input data, layers, nodes, and iterations computation. this paper, attempt has been made to use fuzzy MLP its learning algorithm for system. The training which allows accelerate proce...

In this paper, we consider a problem of parametric identification piece-wise linear mechanical system described by ordinary differential equations. We reconstruct the phase space investigated from accelerometer data and perform parameter using iteratively reweighted least squares. Two key features our study are as follows. First, use differentiated governing equation containing acceleration vel...

This book is aimed at researchers with backgrounds in dynamic systems and control and for use in an advanced graduate-level course. The main subjects cover knowledge acquisition, representation, and utilization in unknown nonlinear dynamic processes using a deterministic framework. This model of information processing, known as deterministic learning, is developed and used for problems of nonli...

  Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...