Unsupervised Learning in Neurodynamics Using the Phase Velocity Field Approach

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

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Michail Zak, Nikzad Toomarian


A new concept for unsupervised learning based upon examples in(cid:173) troduced to the neural network is proposed. Each example is con(cid:173) sidered as an interpolation node of the velocity field in the phase space. The velocities at these nodes are selected such that all the streamlines converge to an attracting set imbedded in the subspace occupied by the cluster of examples. The synaptic interconnections are found from learning procedure providing selected field. The theory is illustrated by examples.

This paper is devoted to development of a new concept for unsupervised learning based upon examples introduced to an artificial neural network. The neural network is considered as an adaptive nonlinear dissipative dynamical system described by the following coupled differential equations:

Ui + K,Ui = L 11j g( Uj ) + Ii