초록 ▼

A recognition apparatus and method using a neural network is provided. A neuron-like element stores a value of its inner condition. The neuron-like element also updates a values of its internal status on the basis of an output from the neuron-like element itself, outputs from other neuron-like eleme

A recognition apparatus and method using a neural network is provided. A neuron-like element stores a value of its inner condition. The neuron-like element also updates a values of its internal status on the basis of an output from the neuron-like element itself, outputs from other neuron-like elements and an external input, and an output value generator a value of its internal status into an external output. Accordingly, the neuron-like element itself can retain the history of input data. This enables the time series data, such as speech, to be processed without providing any special devices in the neural network.

대표청구항 ▼

[ What is claimed is:] [1.] A recognition apparatus comprising:feature extracting means for extracting values of an input to be recognized and for inputting extracted values into a recurrent neural network, anda learning section for causing said recurrent neural network to learn, the learning sectio

[ What is claimed is:] [1.] A recognition apparatus comprising:feature extracting means for extracting values of an input to be recognized and for inputting extracted values into a recurrent neural network, anda learning section for causing said recurrent neural network to learn, the learning section comprising:input data storage means for storing input learning data of a plurality of continuous data streams within a plurality of categories;input data selection means for selecting input learning data of a plurality of continuous data streams to be learned within a plurality of categories from said input data storage means;output data storage means comprising a positive output data storage means and a negative output data storage means for storing output learning data of a plurality of continuous data streams within a plurality of categories each of which corresponds to an input learning data category;output data selection means for selecting output learning data of a plurality of continuous data streams to be learned, each of which corresponds to an input learning data category selected by said input data selection means from said output data storage means;input data connecting means for connecting the input learning data selected by said input data selection means into a single continuous data stream;output data connecting means for connecting the output learning data selected by said output data selection means into a single continuous data stream in correlation with the connection of said input learning data; andlearning control means for inputting said connected input learning data stream into said feature extracting means and for changing weightings at connections of neuron elements on the basis of outputs of said recurrent neural network and said connected output learning data stream.