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Khishe, M., Ebrahimi, E., Goldani, A. (2020). Designing a Sonar System with the Ability of Classifying Active and Passive Acoustic Targets Based on the Evolutionary Neural Network. Electronic and Cyber Defense, 11(2), 191-203.
Mohammad Khishe; Ehsan Ebrahimi; Afshin Goldani. "Designing a Sonar System with the Ability of Classifying Active and Passive Acoustic Targets Based on the Evolutionary Neural Network". Electronic and Cyber Defense, 11, 2, 2020, 191-203.
Khishe, M., Ebrahimi, E., Goldani, A. (2020). 'Designing a Sonar System with the Ability of Classifying Active and Passive Acoustic Targets Based on the Evolutionary Neural Network', Electronic and Cyber Defense, 11(2), pp. 191-203.
Khishe, M., Ebrahimi, E., Goldani, A. Designing a Sonar System with the Ability of Classifying Active and Passive Acoustic Targets Based on the Evolutionary Neural Network. Electronic and Cyber Defense, 2020; 11(2): 191-203.

Designing a Sonar System with the Ability of Classifying Active and Passive Acoustic Targets Based on the Evolutionary Neural Network

علوم و فناوریهای پدافند نوین
Article 8, Volume 11, Issue 2 - Serial Number 40, July 2020, Pages 191-203    PDF (2.39 M)
Document Type: Original Article
Authors
Mohammad Khishe* 1; Ehsan Ebrahimi2; Afshin Goldani3
1Iran university of science and technology
2Marine science university of Imam Khomeini
3marine science university of Imam Khomeini
Receive Date: 10 May 2019,  Revise Date: 05 December 2019,  Accept Date: 14 June 2020 
Abstract
Considering the importance of identifying and determining the nature of the sonar targets in marine battles, the purpose of this paper is to design a system with the ability to classify active and passive sonar targets using multi-layer perceptron neural networks (MLP NNs). Considering the defects of MLP NNs in dealing with real-world data, as well as low classification accuracy and low convergence rate, this paper proposes a new meta-parasitic algorithm called Chaotic Groups Particles Swarm Optimizer (CGPSO) to train an MLP NN. This algorithm explores the search space faster and better than normal particle swarm Optimizer (PSO) using chaotic and independent groups. To evaluate the designed system, a benchmark sonar dataset, a passive laboratory data set and an active dataset were developed. In order to have a comprehensive comparison, the designed system was compared with PSO, biogeography-based Optimizer (BBO) and Gray Wolf Optimizer (GWO) in terms of convergence rate, classification accuracy, and reliability. Results show that the designed system was more accurate than the best available classifier, by average 2.33%.
Keywords
Sonar; Classification; Multi-layer Perceptron; Chaotic Particle Swarm Optimization
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