Paper Info
Title
A multi-agent concurrent neurosimulated annealing algorithm: A case study on intelligent sensing of manhole gases.
Abstract
In this article, we proposed a multi-agent concurrent neorosimulated annealing (CNSA) algorithm, which was used for the supervised training of the neural networks (NN). The proposed CNSA is a population based parallel version of the basic simulated annealing (SA) algorithm. In this work, CNSA was applied for designing an intelligent sensory system that detects proportion of component gases of manhole gas mixture. The proposed intelligent sensory system was modeled using NN, where, the training of NN was supplemented by the proposed parallel version of SA algorithm, that is, CNSA. Once the training of the NN was covered, the sensory system was used for sensing the accumulated toxic gas components of manholes. The manhole gas-mixture problem was treated as pattern recognition and noise reduction problem. This article offers a critical performance analysis of CNSA algorithm, where its performance was compared with backpropagation, conjugate gradient algorithm, particle swarm optimization, and genetic algorithm in both empirical and statistical sense. We found that the proposed CNSA performed significantly well in comparison to its counterparts as far as this case study was concerned.
Year
Venue
Field
2016
Int. J. Hybrid Intell. Syst.
Noise reduction,Conjugate gradient method,Population,Computer science,Artificial intelligence,Artificial neural network,Genetic algorithm,Simulated annealing,Particle swarm optimization,Pattern recognition,Algorithm,Backpropagation,Machine learning
DocType
Volume
Issue
Journal
12
4
Citations 
PageRank 
References 
1
0.35
6
Authors
4
Name
Order
Citations
PageRank
Varun Kumar Ojha1329.25
Paramartha Dutta210020.77
Atal Chaudhuri3104.67
H. Saha4468.61