TY - JOUR N2 - 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 lowconvergence 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 bestcollected 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. L1 - http://journals.pan.pl/Content/110268/PDF/aoa.2019.126360.pdf L2 - http://journals.pan.pl/Content/110268 PY - 2019 IS - No 1 EP - 151 DO - 10.24425/aoa.2019.126360 KW - Multi-Layer Perceptron Neural Network KW - Adaptive Best Mass Gravitational Search Algorithm KW - sonar KW - classification A1 - Mosavi, Mohammad Reza A1 - Khishe, Mohammad A1 - Naseri, Mohammad Jafar A1 - Parvizi, Gholam Reza A1 - Ayat, Mehdi PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 44 DA - 2019.03.11 T1 - Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset SP - 137 UR - http://journals.pan.pl/dlibra/publication/edition/110268 T2 - Archives of Acoustics ER -