Intelligent maximum likelihood self-adjustable block roots assignment for a class of MIMO stochastic systems

Belkacem Bekhiti; George F. Fragulis; Kamel Hariche

doi:10.2478/candc-2025-0014

Abstract

This paper presents a new self-adjustable block roots assignment control scheme for multivariable stochastic systems, via the use of conventional intelligent MIMO maximum likelihood identification algorithm, handled with an adaptive neural based fuzzy inference system (ANFIS). The proposed state-space self-tuning control methodology can be applied to the multivariable stochastic system without requiring prior knowledge of system parameters and noise properties. Illustrative examples demonstrate the effectiveness of the proposed approach.

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Intelligent maximum likelihood self-adjustable block roots assignment for a class of MIMO stochastic systems

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