title: Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.
abstract: Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.
authors.affiliation: Madhav Institute of Technology & Science, Gwalior 474005, India.
authors.forename: Vandana
authors.initials: V
authors.lastname: Sakhre
authors.affiliation: Madhav Institute of Technology & Science, Gwalior 474005, India.
authors.forename: Sanjeev
authors.initials: S
authors.lastname: Jain
authors.affiliation: SGB Amravati University, Amravati 444062, India.
authors.forename: Vilas S
authors.initials: VS
authors.lastname: Sapkal
authors.affiliation: IIT, Delhi 110001, India.
authors.forename: Dev P
authors.initials: DP
authors.lastname: Agarwal
mesh_terms: D003198:Computer Simulation; D017143:Fuzzy Logic; D000069550:Machine Learning; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics