title: A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.
abstract: In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.
authors.affiliation: Institute of Intelligence Science and Technology, Hohai University, Nanjing, China.
authors.forename: Caigen
authors.initials: C
authors.lastname: Zhou
authors.affiliation: Institute of Intelligence Science and Technology, Hohai University, Nanjing, China.
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zeng
authors.affiliation: Department of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, MI, USA.
authors.forename: Chaomin
authors.initials: C
authors.lastname: Luo
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, China.
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001245:Association Learning / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008568:Memory / Q000502:physiology*; D016571:Neural Networks, Computer; D013997:Time Factors
keywords: Associative memory; Asymptotic stability; Biological neural networks; Delay effects; Stability criteria