Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 10; pp. 2319 - 2333
• Main Authors: Pu, Yi-Fei, Yi, Zhang, Zhou, Ji-Liu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analog circuits; Artificial neural networks; Biological neural networks; Calculus; Circuit stability; Cloning; Defense against chip cloning attacks; Descent; Fractional calculus; fractional Hopfield neural networks (FHNNs); fractional-order-sensitivity; fractional-order-stability; Liapunov functions; Long term memory; Lyapunov methods; Neural networks; Recurrent neural networks; Stability analysis

This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attemp...