Finite‐time and fixed‐time synchronization of fuzzy Clifford‐valued Cohen‐Grossberg neural networks with discontinuous activations and time‐varying delays

Aouiti, Chaouki; Bessifi, Mayssa

doi:10.1002/acs.3333

Cited by 16 publications

(1 citation statement)

References 54 publications

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“…In addition, the uncertainty brings another difficult problem to the design of nonlinear system controller. Fortunately, the development of neural network (NN) [9][10][11][12] and fuzzy logic system (FLS) 13,14 provide effective methods to deal with these issues. For a class of nonlinear systems, a neural controller 15 and an adaptive fuzzy controller 16 are designed separately for the tracking performances and the desired stability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural tracking control for non‐affine switched systems with multiple time delays and actuator faults

Zhang

2022

Adaptive Control & Signal

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Summary In this article, an adaptive output feedback neural tracking control algorithm is studied for non‐affine switched systems with multiple time delays and actuator faults. First, an adaptive neural state observer, which only needs to update a parameter online, is presented to estimate the unmeasurable state, and Lipschitz condition is used to deal with delay fault so as to avoid complex delay assumption. Then, by using online estimation information and backstepping technique, a switched fault‐tolerant control mechanism is established for non‐affine switched systems, the designed controller of each subsystems can ensure that non‐affine switched systems output signal can track reference signal under switch signals with average dwell time and all closed‐loop signals are bounded in the presence of multiple time delays, actuator faults, and external disturbances. Finally, simulation results verify the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Adaptive neural tracking control for non‐affine switched systems with multiple time delays and actuator faults

Zhang

2022

Adaptive Control & Signal

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Machine Learning Clifford Invariants of ADE Coxeter Elements

Chen,

Dechant,

et al. 2024

Adv. Appl. Clifford Algebras

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There has been recent interest in novel Clifford geometric invariants of linear transformations. This motivates the investigation of such invariants for a certain type of geometric transformation of interest in the context of root systems, reflection groups, Lie groups and Lie algebras: the Coxeter transformations. We perform exhaustive calculations of all Coxeter transformations for $$A_8$$ A 8 , $$D_8$$ D 8 and $$E_8$$ E 8 for a choice of basis of simple roots and compute their invariants, using high-performance computing. This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning. In this paper we focus on neural network classification and principal component analysis. Since the output—the invariants—is fully determined by the choice of simple roots and the permutation order of the corresponding reflections in the Coxeter element, we expect huge degeneracy in the mapping. This provides the perfect setup for machine learning, and indeed we see that the datasets can be machine learned to very high accuracy. This paper is a pump-priming study in experimental mathematics using Clifford algebras, showing that such Clifford algebraic datasets are amenable to machine learning, and shedding light on relationships between these novel and other well-known geometric invariants and also giving rise to analytic results.

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Weighted Pseudo Almost Periodic Synchronization for Clifford-Valued Neural Networks with Leakage Delay and Proportional Delay

2023

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Finite‐time and fixed‐time synchronization of fuzzy Clifford‐valued Cohen‐Grossberg neural networks with discontinuous activations and time‐varying delays

Cited by 16 publications

References 54 publications

Adaptive neural tracking control for non‐affine switched systems with multiple time delays and actuator faults

Adaptive neural tracking control for non‐affine switched systems with multiple time delays and actuator faults

Machine Learning Clifford Invariants of ADE Coxeter Elements

Weighted Pseudo Almost Periodic Synchronization for Clifford-Valued Neural Networks with Leakage Delay and Proportional Delay

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