초록 ▼

In a neural network, input neuron units of an input layer are grouped into first through J-th input layer frames, where J represents a predetermined natural number. Intermediate neuron units of an intermediate layer are grouped into first through J-th intermediate layer frames. An output layer compr

In a neural network, input neuron units of an input layer are grouped into first through J-th input layer frames, where J represents a predetermined natural number. Intermediate neuron units of an intermediate layer are grouped into first through J-th intermediate layer frames. An output layer comprises an output neuron unit. Each intermediate neuron unit of a j-th intermediate layer frame is connected to the input neuron units of j′-th input layer frames, where j is variable between 1 and j and j′represents at least two consecutive integers, one of which is equal to j and at least one other of which is less than j. Each output neuron unit is connected to the intermediate neuron units of the intermediate layer. For recognition of an input pattern represented by a time sequence of feature vectors, each consisting of K vector components, where K represents a predetermined positive integer, each input layer frame consists of K input neuron units. Each intermediate layer frame consists of M intermediate neuron units, where M represents a positive integer which is less than K. The vector components of each feature vector are supplied to the respective input neuron units of one of the input layer frames that is preferably selected from three consecutively numbered input layer frames. The neural network is readily trained to make a predetermined one of the output neuron units produce an output signal indicative of the input pattern and can be implemented by a microprocessor.

대표청구항 ▼

A neural network for recognizing an input pattern represented by a pattern time sequence of feature vectors which are positioned at respective pattern time instants, said neural network comprising: an input layer of input neuron units grouped into first through J-th input layer frames, where J repre

A neural network for recognizing an input pattern represented by a pattern time sequence of feature vectors which are positioned at respective pattern time instants, said neural network comprising: an input layer of input neuron units grouped into first through J-th input layer frames, where J represents a predetermined natural number; an intermediate layer of intermediate neuron units grouped into first through J-th intermediate layer frames; an output layer comprising an output neuron unit assigned to a predetermined pattern; input-intermediate connections connecting each intermediate neuron unit of a j-th intermediate layer frame to the input neuron units of at least two consecutive input layer frames beginning at the j-th input layer frame and proceeding in a descending order in the direction of (j-1), where j is variable between 1 and J, both inclusive, said input-intermediate connections connecting each intermediate neuron unit of said j-th intermediate layer frame to no input neuron unit when (j-1) is not a positive integer; intermediate-output connections connecting said output neuron unit to the intermediate neuron units of said first through said J-th intermediate layer frames; input means for supplying said feature vectors to the input neuron units of said first through said J-th input layer frames with correspondence established between said pattern time instants and said first through said J-th input layer frames; and adjusting means connected to said input-intermediate and said intermediate-output connections for adjusting said input-intermediate and said intermediate-output connections to make said output neuron unit produce an output signal, said neural network recognizing said input pattern as said predetermined pattern when said adjusting means maximizes said output signal.