[특허]Self-organizing neural network for pattern classification

Self-organizing neural network for pattern classification 원문보기

IPC분류정보
국가/구분	United States(US) Patent 등록
국제특허분류(IPC7판)	G06F-015/18
출원번호	US-0332471 (1994-10-31)
발명자 / 주소	Yoda Fumio (Hillsboro OR)
출원인 / 주소	Mitsubishi Denki Kabushiki Kaisha (Tokyo JPX 03)
인용정보	피인용 횟수 : 23 인용 특허 : 0

초록 ▼

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.

대표청구항 ▼

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.

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IPC	Description
A	생활필수품
A62	인명구조; 소방(사다리 E06C)
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A62B-1/08	.. 윈치 또는 풀리에 제동기구가 있는 것

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Self-organizing neural network for pattern classification 원문보기

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Self-organizing neural network for pattern classification 원문보기

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