Framing regional innovation and technology policies for transformative change

Abstract

Abstract The current state of social and economic development of regions requires new approaches to increasing the efficiency of their activities, and above all scientific approaches to forecasting, as one of the main components of the strategy of transformative changes. It is proposed to use an architecture based on neuro-fuzzy networks for forecasting regional development, which is characterized by a high learning rate due to the linear dependence of outputs on adjustable weights. Scientific and methodological approaches are developed to determine the global minimum of the learning criterion, taking into account the decision rules “if-then”.