Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Ojha, Varun Kumara; b; * | Dutta, Paramarthac | Chaudhuri, Atalb | Saha, Hiranmayd
Affiliations: [a] IT4Innovations, VŠB Technical University of Ostrava, Ostrava, Czech Republic | [b] Department of Computer Science and Engineering, Jadavpur University, Kolkata, India | [c] Department of Computer and System Sciences, Visva-Bharati University, India | [d] CEGESS, Indian Institute of Engineering Science and Technology, Howrah, India
Correspondence: [*] Corresponding author: Varun Kumar Ojha, IT4Innovations, VŠB Technical University of Ostrava, Ostrava, Czech Republic. E-mail:varun.kumar.ojha@vsb.cz
Abstract: In this work, we proposed various strategies for improving the performance of continuous ant colony optimization algorithm (ACO^*), which was used here for optimizing neural network (NN). Here, a real-world problem, that is, detection of manhole gas components, was used for case study. Manhole contains various toxic and explosive gases. Therefore, pre-detection of these toxic gases is crucial to avoid human fatality. Hence, we proposed to design an intelligent sensory system, which used a trained NN for detecting manhole gases. The training to NN was provided using dataset that was generated using laboratory tests, sensor's data-sheets, and literature. The primary focus of this work was on the performance evaluation and improvement of ACO^* algorithm. Hence, understanding of ACO^* parameter tuning and enhancements of ACO^* parameters through %its performance evaluation was well studied. Moreover, complexity analysis of ACO^* was firmly addressed. %in this article. We extended our article scope to cover the performance comparisons between ACO^* and other NN training algorithms. We found that the improved ACO^* performed best in comparison to other NN training algorithms such as backpropagation, conjugate gradient, particle swarm optimization, simulated annealing, and genetic algorithm.
Keywords: Continuous ant colony optimization, neural network, intelligent system, gas detection, complexity analysis
DOI: 10.3233/HIS-160215
Journal: International Journal of Hybrid Intelligent Systems, vol. 12, no. 4, pp. 185-202, 2015
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl