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6 March 2002 Properties of feedback neural networks studied from discrete algebra and N-dimension geometry
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Abstract
Hopfield net is a typical example of a one-layer, feed-back neural network containing a layer of binary neurons and a linear feed-back matrix - the connection matrix. It was first formulated by Hopfield using nonlinear-differential equations and later by Grossberg using differential-integral equations. The nonlinear properties of the network derived form these formulations are scarce and non-systematic because of the difficulty of obtaining the complete solutions form these formulations. We use a simple discrete- algebra formulation which contains only one nonlinear operator - the threshold-logic operator, or the SGN operator. Many interesting and systematic anomalous nonlinear properties can then be derived. These properties include, eigen-state storage, associative storage, domain of attraction, content-addressable recall, fault-tolerant recall, capacity of storage, binary oscillating states, limit-cycles in the state space, and noise-sensitive input states. This paper will describe the physical origins of these anomalous nonlinear properties and the simplified mathematical analysis that leads to the derivation of these properties.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chia-Lun John Hu "Properties of feedback neural networks studied from discrete algebra and N-dimension geometry", Proc. SPIE 4734, Optical Pattern Recognition XIII, (6 March 2002); https://doi.org/10.1117/12.458409
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