QPSO for Training of Ann in Channel Equalization
Padma Charan Sahu1, Sunita Panda2

1Padma Charan Sahu, Kalam Institute of Technology, Berhampur, Odisha, India.
2Sunita Panda, GITAM Deemed to be University, Bengaluru, India.
Manuscript received on 17 April 2019 | Revised Manuscript received on 24 April 2019 | Manuscript published on 30 April 2019 | PP: 599-602 | Volume-8 Issue-6, April 2019 | Retrieval Number: E3108038519/19©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: This basic aim of this article is to find the optimal solution by the use of QPSO for training of Artificial Neural Network channel Equalization. As Particle Swarm Optimization techniques cannot find optimum value easily and also the rate of convergence is low. To overcome this drawback, QPSO is evolved which is capable of finding the optimal solution easily as well as increase the convergence speed. By using the QPSO the parameter like neurons, numbers of segmants etc are optimized. The outcomes guarantee that quantum-based particle swarm optimization technique is better than PSO technique. 
Keyword: QPSO, PSO, Channel Equalization, and Neural Network.
Scope of the Article: Neural Network