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Improvements of Complex-Valued Hopfield Associative Memory by Using Generalized Projection Rules

Published: 01 September 2006 Publication History
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  • Abstract

    In this letter, new design methods for the complex-valued multistate Hopfield associative memories (CVHAMs) are presented. We show that the well-known projection rule proposed by Personnaz can be generalized to complex domain such that the weight matrix of the CVHAM can be designed by using a simple and effective method. The stability of the proposed CVHAM is analyzed by using energy function approach which shows that in synchronous update mode the proposed model is guaranteed to converge to a fixed point from any given initial state. Moreover, the projection geometry of the generalized projection rule (GPR) is discussed. In order to enhance the recall capability, a strategy of eliminating the spurious memories is also reported. The validity and the performance of the proposed methods are investigated by computer simulation

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    • (2020)Accelerating Scientific Computing in the Post-Moore’s EraACM Transactions on Parallel Computing10.1145/33809407:1(1-31)Online publication date: 29-Mar-2020
    • (2020)A broad class of discrete-time hypercomplex-valued Hopfield neural networksNeural Networks10.1016/j.neunet.2019.09.040122:C(54-67)Online publication date: 1-Feb-2020
    • (2019)Finite-Time Synchronization of Complex-Valued Neural Networks with Multiple Time-Varying Delays and Infinite Distributed DelaysNeural Processing Letters10.1007/s11063-018-9958-650:2(1773-1787)Online publication date: 1-Oct-2019
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    Published In

    cover image IEEE Transactions on Neural Networks
    IEEE Transactions on Neural Networks  Volume 17, Issue 5
    September 2006
    248 pages

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    IEEE Press

    Publication History

    Published: 01 September 2006

    Author Tags

    1. Complex-valued Hopfield associative memory (CVHAM)
    2. generalized projection rule (GPR)
    3. spurious memory

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    • (2020)Accelerating Scientific Computing in the Post-Moore’s EraACM Transactions on Parallel Computing10.1145/33809407:1(1-31)Online publication date: 29-Mar-2020
    • (2020)A broad class of discrete-time hypercomplex-valued Hopfield neural networksNeural Networks10.1016/j.neunet.2019.09.040122:C(54-67)Online publication date: 1-Feb-2020
    • (2019)Finite-Time Synchronization of Complex-Valued Neural Networks with Multiple Time-Varying Delays and Infinite Distributed DelaysNeural Processing Letters10.1007/s11063-018-9958-650:2(1773-1787)Online publication date: 1-Oct-2019
    • (2019)Bifurcation Analysis of Delayed Complex-Valued Neural Network with DiffusionsNeural Processing Letters10.1007/s11063-018-9899-050:2(1019-1033)Online publication date: 1-Oct-2019
    • (2018)Hyperbolic Hopfield neural networks with directional multistate activation functionNeurocomputing10.1016/j.neucom.2017.10.053275:C(2217-2226)Online publication date: 31-Jan-2018
    • (2018)Multistate vector product hopfield neural networksNeurocomputing10.1016/j.neucom.2017.07.013272:C(425-431)Online publication date: 10-Jan-2018
    • (2018)Personality affected robotic emotional model with associative memory for human-robot interactionNeurocomputing10.1016/j.neucom.2017.06.069272:C(213-225)Online publication date: 10-Jan-2018
    • (2018)Fixed points of symmetric complex-valued Hopfield neural networksNeurocomputing10.1016/j.neucom.2017.05.006275:C(132-136)Online publication date: 31-Jan-2018
    • (2017)Multistability of complex-valued neural networks with distributed delaysNeural Computing and Applications10.5555/3041299.316900428:1(1-14)Online publication date: 1-Jan-2017
    • (2017)Fast Recall for Complex-Valued Hopfield Neural Networks with Projection RulesComputational Intelligence and Neuroscience10.1155/2017/48942782017Online publication date: 1-Jan-2017
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