US 5,479,575 A · Self-organizing neural network for pattern classification

Abstract

A neural network includes a plurality of input nodes for receiving the respective elements of the input vector. A copy of all of the elements of the input vector is sent to the next level of nodes in the neural network denoted as intermediate nodes. The intermediate nodes each encode a separate template pattern. They compare the actual input pattern with the template and generate a signal indicative of the difference between the input pattern and the template pattern. Each of the templates encoded in the intermediate nodes has a class associated with it. The difference calculated by the intermediate nodes is passed to an output node for each of the intermediate nodes at a given class. The output node then selects the minimum difference amongst the values sent from the intermediate nodes. This lowest difference for the class represented by the output node is then forwarded to a selector. The selector receives such values from each of the output nodes of all of the classes and then selects that to output value which is a minimum difference. The selector in turn, generates a signal indicative of the class of the intermediate node that sent the smallest difference value.

Claims21

Dependent claims

A self-organizing neural network for classifying an input pattern signal having a plurality of elements, which comprises:

a) input buffers, each having an input for receiving an element of the input pattern signal, and an output which provides a copy of the element of the input pattern signal;
b) intermediate nodes, each having an input connected to an output of each input buffer via first signal lines, an output through which an intermediate output value is provided, and means for storing weighting factors;
c) output nodes, each having inputs connected to an output of each intermediate node of a class via second signal lines, each output node determining a minimum value among output values from the intermediate nodes of the class, and each having an output through which a class signal is provided as an indication of an intermediate node which provided said minimum value, and further as an indication of the minimum value;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which provided the minimum value output, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern signal, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern signal, to compare the selected class to the correct class indicated by the teaching signal and, when the selected class is the same as the correct class, to generate a learning signal so that weighting factors of said intermediate node are modified based on said learning signal if a difference between the output value of the output node of the correct class and the output value for the output node which has a second smallest output value of all of the output nodes is below a predetermined threshold.

2 more elements

A self-organizing neural network system for classifying an input pattern, comprising:

a) input buffers, each having an input for receiving an element of an input pattern signal, and an output which provides a copy of said input pattern signal;
b) intermediate nodes, each having an input connected to an output of each input buffer via first signal lines, and an output through which an output value is provided;
c) output nodes, each having inputs connected to an output of each intermediate node of a class via second signal lines, each output node determining a minimum value among output values from the intermediate nodes of the class, and each having an output through which a class signal is provided as an indication of an intermediate node which provided said minimum value, and further as an indication of the minimum value;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which provided the minimum value output, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern signal, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern, to compare the selected class to the correct class indicated by the teaching signal and, when the selected class is not the same as the correct class, to generate a learning signal so that an intermediate node and an output node are created for the correct class based on the input pattern if an output node for the correct class does not exist.

2 more elements

A self-organizing neural network system for classifying an input pattern, comprising:

a) input buffers, each having an input for receiving an element of an input pattern signal, and an output which provides a copy of said input pattern signal;
b) intermediate nodes, each having an input connected to an output of each input buffer via first signal lines, and an output through which an output value is provided;
c) output nodes, each having inputs connected to an output of each intermediate node of a class via second signal lines, each output node determining a minimum value among output values from the intermediate nodes of the class, and each having an output through which a class signal is provided as an indication of the intermediate node which provided said minimum-value, and further as an indication of the minimum value;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which has the minimum value output, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern signal, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern, to compare the selected class to the correct class indicated by the teaching signal and, when the selected class is not the same as the correct class and a difference between each template of the correct class and the input pattern signal is greater than a predetermined threshold, to generate a learning signal so that an intermediate node connected to the output node for the correct class is created having weighting factors based on the input pattern signal.

2 more elements

A self-organizing neural network system for classifying an input pattern, comprising:

a) input buffers, each having an input for receiving an element of an input pattern signal, and an output which provides a copy of the element of the input pattern signal;
b) intermediate nodes, each having an input connected to an output of said input buffers via first signal lines, and an output through which an output value is provided;
c) output nodes, each having inputs connected to an output of each intermediate node of a class via second signal lines, each output node determining a minimum value among output values from the intermediate nodes of the class, and each having an output through which a class signal is provided as an indication of an intermediate node which provided said minimum value, and further as an indication of the minimum value;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which provided the minimum value output, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern signal, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern, to compare the selected class to the correct class indicated by the teaching signal and, when the selected class is the same as the correct class, to generate a learning signal so that weighting factors are adjusted so as to ensure stable classification when the correct class has not been stably classified, and so that weighting factors are adjusted according to a determined reason for any incorrect classification to improve a likelihood of correct classification when the determined reason exists, and further so that the weighting factors of only the intermediate node of the correct class which is most similar to the input pattern are adjusted when a reason for incorrect classification cannot be determined.

2 more elements

A self-organizing neural network, for classifying an input pattern represented by an input pattern signal, which comprises:

a) input buffers, each having an input for receiving a scalar element of said input pattern signal, and an output which provides a copy of said scalar element;
b) intermediate nodes, each having an input connected to the outputs said input buffers via first signal lines, means for storing weighting factors indicative of a template pattern, and an output which provides an intermediate output value indicative of a measure of similarity of the input pattern to the template pattern;
c) output nodes, each defining a class and having inputs connected to the output of each intermediate node of the class via second signal lines, each output node determining, from the intermediate output values from the intermediate nodes of the class, which intermediate node has a template pattern most similar to the input pattern, and each output node having an output through which a class signal is provided as an indication of the intermediate node of the class which has a template pattern most similar to the input pattern and the indication of the intermediate output value of said intermediate node;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which has a template pattern most similar to the input pattern, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern, to compare the selected class to the correct class indicated by the teaching signal and, when the selected class is the same as the correct class, to generate a learning signal so that weighting factors of said intermediate node are modified based on said learning signal if a difference between the intermediate output value indicated by the class signal of the output node of the correct class and the intermediate output value indicated by the class signal for the output node connected to an intermediate node with a template pattern which is second most similar to the input pattern is below a predetermined threshold.

2 more elements

A self-organizing neural network, for classifying an input pattern represented by an input pattern signal, which comprises:

a) input buffers, each having an input for receiving a scalar element of said input pattern signal, and an output which provides a copy of said scalar element;
b) intermediate nodes, each having an input connected to the outputs said input buffers via first signal lines, means for storing weighting factors indicative of a template pattern, and an output which provides an intermediate output value indicative of a measure of similarity of the input pattern to the template pattern;
c) output nodes, each defining a class and having inputs connected to the output of each intermediate node of the class via second signal lines, each output node determining, from the intermediate output values from the intermediate nodes of the class, which intermediate node has a template pattern most similar to the input pattern, and each output node having an output through which a class signal is provided as an indication of the intermediate node of the class which has a template pattern most similar to the input pattern and the indication of the intermediate output value of said intermediate node;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which has a template pattern most similar to the input pattern, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern, to compare the selected class to the correct class indicated by the teaching signal and, when the selected class is not the same as the correct class, to generate a learning signal so that an intermediate node and an output node are created for the correct class based on the input pattern if an output node for the correct class does not exist.

2 more elements

A self-organizing neural network for classifying an input pattern represented by an input pattern signal, which comprises:

a) input buffers, each having an input for receiving a scalar element of said input pattern signal, and an output which provides a copy of said scalar element;
b) intermediate nodes, each having an input connected to the outputs said input buffers via first signal lines, means for storing weighting factors indicative of a template pattern, and an output which provides an intermediate output value indicative of a measure of similarity of the input pattern to the template pattern;
c) output nodes, each defining a class and having inputs connected to the output of each intermediate node of the class via second signal lines, each output node determining, from the intermediate output values from the intermediate nodes of the class, which intermediate node has a template pattern most similar to the input pattern, and each output node having an output through which a class signal is provided as an indication of the intermediate node of the class which has a template pattern most similar to the input pattern and the indication of the intermediate output value of said intermediate node;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which has a template pattern most similar to the input pattern, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern, to compare the selected class to the correct class indicated by the teaching signal and, when the selected class is not the same as the correct class and a difference between each template pattern of the correct class and the input pattern is greater than predetermined threshold, to generate a learning signal so that an intermediate node connected to the output node for the correct class is created having weighting factors based on the input pattern.

2 more elements

A self-organizing neural network for classifying an input pattern represented by an input pattern signal, which comprises:

a) input buffers, each having an input for receiving a scalar element of said input pattern signal, and an output which provides a copy of said scalar element;
b) intermediate nodes, each having an input connected to the outputs said input buffers vie first signal lines, means for storing weighting factors indicative of a template pattern, and an output which provides an intermediate output value indicative of a measure of similarity of the input pattern to the template pattern;
c) output nodes, each defining a class and having inputs connected to the output of each intermediate node of the class via second signal lines, each output node determining, from the intermediate output values from the intermediate nodes of the class, which intermediate node has a template pattern most similar to the input pattern, and each output node having an output through which a class signal is provided as an indication of the intermediate node of the class which has a template pattern most similar to the input pattern and the indication of the intermediate output value of said intermediate node;
and
d) a self-organizing selector having first inputs connected to the outputs of the output nodes, a first output through which a response signal is provided to an external component, the response signal indicating the class of the intermediate node which has a template pattern most similar to the input pattern, a second output through which a learning signal is provided to said intermediate nodes via third signal lines, and a second input adapted to receive a teaching signal from an external component, the teaching signal indicating a correct class for said input pattern, said self-organizing selector being responsive to said inputs so as to select a class for the input pattern, to compare the selected class to the correct class indicated by the teaching signal and to generate a learning signal so that weighting factors of said intermediate nodes are adjusted so as to ensure stable classification when the correct class has not been stably classified, and so that weighting factors of said intermediate nodes are adjusted according to a determined reason for any incorrect classification to improve a likelihood of correct classification, and so that the weighting factors of an intermediate node of only the correct class are adjusted when a reason for incorrect classification cannot be determined.

2 more elements

Dependencies are inferred from the claim text.

Self-organizing neural network for pattern classification

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Self-organizing neural network for pattern classification

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Abstract

Claims21