Narrow Results

This paper introduces a novel identifier scheme for identification of nonlinear systems with disturbances. The identification process is carried out in two steps: an offline procedure and an online procedure. The method comprises of an automatic structure generating phase using entropybased technique. The accuracy of the model is suitably controlled using the entropy measure. The parameter learning phase uses the backpropagation technique. To improve the accuracy and also for generalization of the model to handle different data sets, Differential Evolution technique is employed whereby the parameters of the model are suitably tuned using evolutionary technique. A semi serial-parallel model is introduced to improve the online identification process in the presence of noisy data. The proposed mechanism is utilized and compared against the classical Sugeno, adaptive network-based fuzzy inference system (ANFIS) modeling and Laguerre Network-Based Fuzzy System for the identification of a nonlinear benchmark problem. In addition, the proposed technique is also used to model a rotary wing unmanned aerial vehicle (UAV) from real test input–output data. The modeling performance and generalization capability are seen to be superior with our method.