A Hybrid LSTM–DNN Model with Fuzzy Inference for Adaptive Dispatcher Control of Industrial Information‑Control Systems

Abstract

Erroneous and noisy measurements significantly distort the assessment of the stability of technological lines in industrial information and control systems. The paper proposes an algorithm for operational dispatch control of technological complexes based on the assessment of noise immunity using a hybrid LSTM–DNN model and a fuzzy set apparatus. The hybrid model predicts time dependencies and failure probabilities, while the fuzzy inference system combines objective sensor data and subjective expert assessments into a single reliability indicator. The use of trapezoidal membership functions and defuzzification by the center of gravity method ensures a smooth mapping between linguistic and numerical variables. An analysis of the monotonic dependence of the interference immunity coefficient on system performance and conditions for maintaining stability in linearly connected storage lines was carried out. Modelling has shown that the proposed neuro-fuzzy approach increases the adaptability and stability of the system to non-stationary disturbances compared to probabilistic methods. The developed model can be integrated into industrial dispatch control systems for real-time decision-making.