GAM: A General Auto-Associative Memory Model

参考文献33件
  • SHI Hongchi
    Department of Contputer Engineering & Computer Science, The University of Missouri-Colurnbia
  • ZHAO Yunxin
    Department of Contputer Engineering & Computer Science, The University of Missouri-Colurnbia
  • ZHUANG Xinhua
    Department of Contputer Engineering & Computer Science, The University of Missouri-Colurnbia
  • REN Fuji
    Faculty of Engineering, The University of Tokushima

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This paper attempts to establish a theory for a general auto-associative memory model. We start by defining a new concept called supporting function to replace the concept of energy function. As known, the energy function relies on the assumption of symmetric interconnection weights, which is used in the conventional Hopfield auto-associative memory, but not evidenced in any biological memories. We then formulate the information retrieving process as a dynamic system by making use of the supporting function and derive the attraction or asymptotic stability condition and the condition for convergence of an arbitrary state to a desired state. The latter represents a key condition for associative memory to have a capability of learning from variant samples. Finally, we develop an algorithm to learn the asymptotic stability condition and an algorithm to train the system to recover desired states from their variant samples. The latter called sample learning algorithm is the first of its kind ever been discovered for associative memories. Both recalling and learning processes are of finite convergence, a must-have feature for associative memories by analogy to normal human memory. The effectiveness of the recalling and learning algorithms is experimentally demonstrated.

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詳細情報 詳細情報について

  • CRID
    1574231877209057280
  • NII論文ID
    110003210667
  • NII書誌ID
    AA10826272
  • ISSN
    09168532
  • 本文言語コード
    en
  • データソース種別
    • CiNii Articles

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