Application of Firefly Algorithm based Neural Network Controller
T. Rathimala1, M. Kamarasan2
1T. Rathimala, Assistant Professor, Department of Computer and Information Science, Annamalai University, Annamalai Nagar, India.
2M. Kamarasan, Assistant Professor, Department of Computer and Information Science, Annamalai University, Annamalai Nagar, India.
Manuscript received on 20 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 2659-2662 | Volume-8 Issue-11, September 2019. | Retrieval Number: K19800981119/2019©BEIESP | DOI: 10.35940/ijitee.K1980.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Firefly Algorithm is comparison of new optimize procedure based on PSO as tautness. The paper presents the competence and forcefulness of the Firefly algorithm as the optimize concept for a proportional–integral–derivative organizer under various loading conditions. The proposed PID controller is attempt to designed and implemented to frequency-control of a two area interconnected systems. The hidden layer formation is not personalized, as the interest lies only on the reckoning of the weights of the system. In sequence to obtain a practicable report, the weights of the neural network are computational or optimized by minimizing function cost or error. A Firefly Algorithm is an efficient but uncomplicated meta-heuristic optimization technique inspired by expected motion of fireflies towards more light, is used for the preparation of neural network. The simulation report view that the calculation competence of training progression using Firefly Optimization performance with Load frequency control. A study of the output report of the system PID controller and FA based neural network controllers are made for 1% change in load in area 1 and it is found that the proposed controllers ensures a better steady state response of the systems.
Keywords: Neural Network (NN), Firefly Algorithm (FA), Optimization Technique, Proportional-Integral-Derivative (PID) Controller, Load Frequency Controls (LFC).
Scope of the Article: Neural Information Processing