Affiliations: [a] Department of Electrical & Computer Engineering, Dalhousie University Halifax, Canada
| [b] Department of Automatics and Applied Software, University “Aurel Vlaicu” Arad, Romania
| [c] Deparment of Electrical Engineering, The University of Lahore, Pakistan
| [d] Center for Automotive Research, The Ohio State University, Columbus, OH USA | [e] Department of Electrical Engineering, University of the Punjab, Pakistan
Abstract: This paper proposes an online algorithm for identifying the nonlinear dynamical systems and is termed as neo-fuzzy based brain emotional learning plant identifier (NFBELPI). As the name suggests, the proposed identifier is a combination of brain emotional learning network and neo-fuzzy neurons. The integration of these two networks is realized in a way that retains the characteristics of both the networks while an enhanced performance is achieved at the same time. Precisely, the orbitofrontal cortex section of the brain emotional learning network is fused with neo-fuzzy neurons with a view to equip it with more knowledge than does the amygdala section possesses. The proposed identifier accepts n-input and m-output samples to generate an estimate of the plant output and employs a brain emotional learning algorithm to lower the estimation error by adjusting a total of ((n + m + 1) × p) + (n + m + 2) weights, with p being the number of neo-fuzzy neurons. The proposal is validated in a MATLAB programming environment using a simulated Narendra dynamical plant as well as against the data recorded from real forced duffing oscillator. Comparison with a brain emotional learning plant identifier (BELPI) and some other state-of-the art identifiers in terms of root mean squared error (RMSE) criterion reveals the improved performance of the proposed identifier.
Keywords: System identification, brain emotional learning, neo-fuzzy neurons, MATLAB
