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Abstract

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.
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Abstract

The subject of research was urban and urban-rural communes in the peripheral areas of voivodships, that is outside the functional areas of their capitals and outside the Silesian agglomerations. The aim of the research was to: (1) recognize the most economically developed entities in the studied areas, (2) recognize how development factors and their combinations that can create territorial capital are perceived and used in municipal strategies. The methods included: (1) analysis of indicators (2) analysis of texts of 10 strategies of entities with a high level of development. It was found, that there was deficiency of specific factors of development and recognition of their combination as well as the lack of using them to create a competitive advantage.
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