Abstract
Neuro-fuzzy models of management nowadays are becoming more widespread in various industries. Many papers deal with the synthesis of neuro-fuzzy models of diagnostics in economics, medicine, and industrial tasks. Most of them are based on iterative topologies of artificial neural networks and traditional fuzzy inference systems. The latter ones do not always ensure high accuracy, which affects the entire system that is being developed. This paper presents a new neuro-fuzzy diagnostic system based on non-iterative ANN and a new fuzzy model, a T-controller. The flowchart of the system proposed is given. All the operation stages of the system are described in detail, from the collection and preliminary processing of data to two different stages of diagnostics. The last stage can be performed manually or using a T-controller. Simulation of the system is conducted by means of a real set of data. The task was to predict the generator power based on a set of 13 independent variables. To improve the accuracy of the system performance, the Padé polynomial has been used, the coefficients of which are synthesized on the basis of a pre-trained SGTM neural-like structure using an optimization simulated annealing simulation. High accuracy of the system performance is shown using various performance indicators. The coefficients of the required polynomial are synthesized, which allows of fulfilling the task in a more understandable form.
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Tkachenko, R., Izonin, I., Tkachenko, P. (2022). Neuro-Fuzzy Diagnostics Systems Based on SGTM Neural-Like Structure and T-Controller. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_47
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DOI: https://doi.org/10.1007/978-3-030-82014-5_47
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