Neuro-fuzzy model of resource provision of innovative activity of an industrial enterprise

A neuro-fuzzy model of resource provision of innovative activity of an industrial enterprise is proposed. The model implements a two-stage procedure for describing and managing innovative activity of an industrial enterprise: at the first stage, interaction resources are classified based on the supplemented VRIO analysis of the interaction profile; at the second stage, an innovative activity strategy is selected. The neuro-fuzzy model of resource provision is based on stacking of private machine learning models, such as the k-nearest neighbors method, random forest, and multilayer perceptron. The classification results of private models are combined using a trained tree of fuzzy inference systems that performs the final classification, which ensures an increase in its accuracy compared to individual private models. A distinctive feature of the model is the use of a fuzzy logical inference system to assess the probability of resource availability used in planning the need for it, which allows taking into account expert judgments as input data. Testing of the neuro-fuzzy model, carried out in the MatLab software system using the example of solving the problem of assessing the resource provision of an innovation process during the interaction of a regional instrument-making enterprise with one of the counterparties, demonstrated the model’s performance and high accuracy of classifying the resources of innovative interaction.