Archives of Acoustics, 44, 1, pp. 137–151, 2019
10.24425/aoa.2019.126360

Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset

Mohammad Reza MOSAVI
http://www.iust.ac.ir/find.php?item=35.11253.45056.en
Iran University of Science and Technology
Iran, Islamic Republic of

Mohammad KHISHE
Iran University of Science and Technology
Iran, Islamic Republic of

Mohammad Jafar NASERI
University of Marine Sciences
Iran, Islamic Republic of

Gholam Reza PARVIZI
Alborz Institute for Higher Education
Iran, Islamic Republic of

Mehdi AYAT
Iran University of Science and Technology
Iran, Islamic Republic of

In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as low-convergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the best-collected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.
Keywords: Multi-Layer Perceptron Neural Network; Adaptive Best Mass Gravitational Search Algorithm; sonar; classification
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DOI: 10.24425/aoa.2019.126360

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