Performance analysis of novel adaptive model for non-linear dynamics system identification

Tasks of system identification has occupied an important space in research field for development of automated system. Artificial neural network (ANN) model is most suitable for analysis of dynamic systems. It has been exploited in this work as an alternative approach for such task. The objective of this paper is to design a novel technique to improve the performance of the existing techniques. Adaptive learning algorithm is applied with the sliding mode strategy for the neuron models. It is considered for the first-order dynamic system with adjustable parameters. It can perform for faster convergence with robust characteristics. It has been chosen as suitable alternative for nonlinear system identification as it has good function approximation capabilities. It has been shown that the proposed ANN model can be used to model the complex dynamic systems. Also the performance analysis has been done using different methods like Sliding Mode strategy, MLP-Back propagation, FLANN-LMS and compared for system identification.