A Neuro-Fuzzy System for Selecting Optimal Environmental Measures
The purpose of the research is the creation of a new neuro-fuzzy system for choosing optimal environmental protection measures. The research tasks are the development of estimated indicators characterizing the compliance of resource-intensive industries with the requirements of the rational use of natural resources; development of an integral indicator; design and training of a neuro-fuzzy system for the selection of optimal measures. To date, there are no universal methods for comparing various industries and technologies with each other for their compliance with the principles of environmental friendliness. The article describes new approaches to assessing and analyzing the degree of compliance of resource-intensive industries with the requirements and standards of rational environmental management. Based on the author’s methodology, a complex indicator is pro-posed that reflects environmental friendliness, taking into account resource consumption and waste generation. The choice of the optimal list of environmental protection measures is carried out by a neural network (NN) with fuzzy logic, which, due to the exclusion of subjectivity, makes it possible to increase efficiency by 25-30 %. Methods: Methods of mathematical analysis, fuzzy set theory and neural networks were used. Novelty: A new integrated solution in the form of a decision support system has been obtained, which allows both to assess the environmental friendliness of resource-intensive industries and technologies, and to propose optimal environmental protection measures to improve their status.