Patent ID: 6260032
Filing Date: 2001-07-10
Classification: G06F

Abstract:
A method for teaching functions of an artificial neural network in a computer module and for multivalued mapping for providing a method for approximation of a manifold in (n+m)-dimensional space, by learning a smooth function from n-dimensional input space to m-dimensional output space which optimally approximates m-dimensional vector value data forming a given plurality of layers in n-dimensional space, said method comprising the steps of:(a) mathematically expressing a multivalued function directly in Kronecker's tensor product form;(b) developing and replacing the tensor product form so as to obtain a linear equation with respect to unknown functions;(c) defining the sum of a linear combination of local base functions and a linear combination of polynomial bases with respect to the replaced unknown functions;(d) learning from example data, a manifold which is defined by the linearized function in the input-output space, through use of a procedure for optimizing the error and the smoothness constraint; and(e) applying the manifold learned from the steps (a) to (d) to an artificial neuro-circuit network, to visual or auditory pattern information processing by a computer, or to kinetic control of a robot arm.