ANALYSIS OF SUSTAINABLE DEVELOPMENT USING FUZZY LOGIC PREDICTION MODELS AND ARTIFICAL NEURAL NETWORKS
Sustainable development is a priority of policies in countries all over the world, regardless of their level of development; this is a dynamic and complex concept based on indicators with vague and difficult to measure characteristics such as resources, labor, education, infrastructure, the existence of modern equipment to ensure manufacturing performance and flexibility. A model of approach and analysis of sustainable development using these indicators with vague characteristics can be achieved by combining prediction models: artificial neural networks and fuzzy logic. Artificial neural networks are used in the study, as they have the advantage of working with hidden layers, and recursive backpropagation algorithms to predict the size of indicators for a certain period, while fuzzy logic is used for three-dimensional interpretation of interdependencies and trends of indicators. The model provides long-term, flexible management decisions by eliminating bottlenecks and assessing deviations from a target defined so that the final result ensures a fast and flexible solution through fast and durable reconfiguring.
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