STORING PATTERNS IN HAMILTONIAN NEURAL NETWORKS

Alexei B. Potapov () and M. K. Ali ()
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Alexei B. Potapov: Department of Physics, The University of Lethbridge, 4401 University Dr. W. Lethbridge, Alberta T1K 3M4, Canada
M. K. Ali: Department of Physics, The University of Lethbridge, 4401 University Dr. W. Lethbridge, Alberta T1K 3M4, Canada

International Journal of Modern Physics C (IJMPC), 2001, vol. 12, issue 08, 1217-1228

Abstract: We consider a number of techniques for storing patterns in orthogonal normal modes of a Hamiltonian system. Such techniques, along with the method of selecting the most excited mode, enable us to introduce a new class of pattern recognition systems that we call Hamiltonian neural networks. In contrast to all traditional neural networks, our Hamiltonian neural networks are nondissipative. Quantization of the Hamiltonian of our network is expected to serve as a means for future work on quantum analogs of classical information processing.

Keywords: Hamiltonian System; Pattern Recognition; Neural Networks (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1142/S0129183101002462

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