Equalization of Supervised Data Trained RBFNN using MSFLA
Sunita Panda1, Padma Charan Sahu2

1Sunita Panda, Department of Electronics and Communication Engg, GITAM Deemed to be University, Bengaluru (Karnataka), India.
2Padma Charan Sahu, Department of Electronics and Communication Engg Kalam Institute of Technology, Berhampur (Odisha), India.

Manuscript received on 18 December 2018 | Revised Manuscript received on 27 December 2018 | Manuscript published on 30 December 2018 | PP: 143-145 | Volume-8 Issue-2, December 2018 | Retrieval Number: B5588128218/18©BEIESP
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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: In order to avoid the channel distortion in signal processing recently, RBFNN based equalizers is mentioned. Hit and trail method is the main provocation problem for design of RBFNN Equalizer. Here the initiation is start with use of the population based optimization algorithm trained RBFNN equalizer, such as Shuffled Frog-Leaping Algorithm as well as its modified forms. The observation is made on the basis of its performance as compared to the other equalizers.
Keywords: RBFNN, Equalization Technique, SFLA.

Scope of the Article: WSN