Weighted pseudo almost automorphic solutions for neutral type fuzzy cellular neural networks with mixed delays and <i>D</i> operator in Clifford algebra
Chaouki Aouiti, Farah Dridi
Abstract
The main aim of this work is to study a class of neutral type Fuzzy Cellular Neural Networks (FCNNs) with mixed delays and D operator. New criteria are established for the existence, uniqueness and global exponential stability of weighted pseudo almost automorphic solutions for the addressed model in Clifford algebra. Our approach is based on Banach's fixed point principle and differential inequality techniques. Finally, numerical example is given to demonstrate the effectiveness of the theoretical results.
Topics & Concepts
MathematicsUniquenessType (biology)Clifford algebraOperator (biology)Fuzzy logicBanach algebraClass (philosophy)Exponential stabilityAlgebra over a fieldStability (learning theory)Artificial neural networkPure mathematicsApplied mathematicsBanach spaceMathematical analysisComputer scienceNonlinear systemGeneEcologyBiologyRepressorMachine learningPhysicsArtificial intelligenceBiochemistryTranscription factorChemistryQuantum mechanicsNeural Networks Stability and SynchronizationCooperative Communication and Network CodingOpinion Dynamics and Social Influence