초록 ▼

A method is described to improve the data transfer rate between a personal computer or a host computer and a neural network implemented in hardware by merging a plurality of input patterns into a single input pattern configured to globally represent the set of input patterns. A base consolidated vec

A method is described to improve the data transfer rate between a personal computer or a host computer and a neural network implemented in hardware by merging a plurality of input patterns into a single input pattern configured to globally represent the set of input patterns. A base consolidated vector (U'*n) representing the input pattern is defined to describe all the vectors (Un, . . . , Un+6) representing the input patterns derived thereof (U' n, . . . , U'n+6) by combining components having fixed and 'don't care' values. The base consolidated vector is provided only once with all the components of the vectors. An artificial neural network (ANN) is then configured as a combination of sub-networks operating in parallel. In order to compute the distances with an adequate number of components, the prototypes are to include also components having a definite value and 'don't care' conditions. During the learning phase, the consolidated vectors are stored as prototypes. During the recognition phase, when a new base consolidated vector is provided to ANN, each sub-network analyses a portion thereof After computing all the distances, they are sorted one sub-network at a time to obtain the distances associated to each vector.

대표청구항 ▼

What is claimed is: 1. A method for improving the data transfer rate of input patterns, referred to as vectors, between a host computer and an artificial neural network (ANN) implemented in hardware during a recognition phase comprising the steps of: a) providing a set of vectors (Un, . . . ) havi

What is claimed is: 1. A method for improving the data transfer rate of input patterns, referred to as vectors, between a host computer and an artificial neural network (ANN) implemented in hardware during a recognition phase comprising the steps of: a) providing a set of vectors (Un, . . . ) having q components; b) merging a set of k consecutive vectors into a single vector having p components, referred to as a base consolidated vector (U'*n), configured to globally represent said set of vectors, said base consolidated vector having only once all the components of the vectors, the first q components being formed by the components of first vector of said vector set, the (p-q) components consisting of the last component of the remaining vectors; c) providing an artificial neural network (ANN) configured as a combination of k sub-networks operating in parallel, each neuron storing at least p components; d) creating a set of k consolidated vectors (U'*n, . . . ,) having p components derived from said base consolidated vector, wherein each consolidated vector has q components in common with the base consolidated vector, the (p-q) remaining components being assigned a don't care condition, the first q components of the first consolidated vector having a direct correspondence with the first q components of the base consolidated vector, with the remaining components remaining as don't care, and repeating the process until the last consolidated vector which has its q last components in direct correspondence with the q last components of the base consolidated vector, the first (p-q) components having a don't care condition; and e) presenting a new base consolidated vector to each sub-network of said ANN for recognition, wherein each of said sub-networks analyses in parallel a portion of said base consolidated vector. 2. The method as recited in claim 1, wherein step a) each vector is derived from the preceding one by deleting the component at the first position and adding a new component at the last position. 3. The method as recited in claim 1, wherein step c) the artificial neural network (ANN) is configured as a combination of k sub-networks operating in parallel by a select command of the neurons comprising said ANN. 4. The method as recited in claim 1, wherein step d) each consolidated vector becomes a prototype of said sub-network of said ANN. 5. The method as recited in claim 4, wherein the first consolidated vector is stored in the first sub-network, and continuing the process until the last consolidated vector is stored in the last sub-network.