A software platform demonstrator for configuring ANFIS neural network hyperparameters in fuzzy systems

This article describes the research demonstrator for experimental verification and evaluation of fuzzy algorithms and neural networks in an expert system for complex multi-stage technological processes. The demonstrator development purpose is to create a scientific and technical foundation for the ready-to-implement solutions transfer to the next project stages. The demonstrator allows assessing the readiness level of the components being developed, conducting research tests, checking the operability and efficiency of the software implementations functioning proposed at various parameter values and their combinations. A complex multi-stage technological process state diagnostics involves the joint primary data processing to obtain probabilistic abnormal critical events or incidents characteristics under conditions of uncertainty. The authors propose a way of using a fuzzy neural network, which is trained with data generated by belief functions. The approach makes it possible to significantly speed up calculations and to minimize the resource base. The article focuses on describing the neural network models and training datasets management, neural network training and quality control, the technological process diagnostics in various modes. The configurable hyper-parameters of the neural network are described in detail. There are examples of the diagnostic procedures implementation in various modes. It is shown that with the software diagnostic system functioning in conditions close to real, the initial assumptions concerning the time reduction for detecting and predicting incidents can be verified and experimentally substantiated. In addition, the technological chains sets that are the incidents causes can be more accurately determined.