초록 ▼

Pattern categorization is provided by a self-organizing analog field/layer which learns many-to-many, analog spatiotemporal mappings. The field/layer employs a set of input nodes, each input node having two long term memory weights, and a set of output nodes. Each input node is for categorizing patt

Pattern categorization is provided by a self-organizing analog field/layer which learns many-to-many, analog spatiotemporal mappings. The field/layer employs a set of input nodes, each input node having two long term memory weights, and a set of output nodes. Each input node is for categorizing patterns with respect to a plurality of categories. The long term memory weights of an input node encode the patterns categorized by the input node. Each input node generates signals as a function of respective long term memory weights and input signals to the input node. Each input node is coupled to a different output node. Each output node receives signals generated by the respective input node and selects a category of the respective input node. The output nodes provide a mapping between plural parts of the input pattern and plural categories of the input nodes. Category selections of the output nodes are modified such that sum of the output signals from the output nodes is within a predefined range. Upon the sum of the output signals being within the predefined range, the output nodes provide categorization of the input pattern from the mapping between plural parts of the input pattern and plural categories of the input nodes.

대표청구항 ▼

A pattern categorization system comprising: a presentation field for presenting input signals defining an input pattern; a plurality of input nodes, each for categorizing patterns with respect to a plurality of categories, the input nodes being coupled to the presentation field for receiving the inp

A pattern categorization system comprising: a presentation field for presenting input signals defining an input pattern; a plurality of input nodes, each for categorizing patterns with respect to a plurality of categories, the input nodes being coupled to the presentation field for receiving the input signals, for each input signal received by an input node, the input node having two weights having a respective value, the weights being indicative of a plurality of patterns categorized by the input node such that a net signal is generated as a function of the weights and input signals to the input node, and the net signal is indicative of similarity between the input pattern and patterns categorized by the input node; a plurality of output nodes, one for each input node such that a different input node is connected to a different output node and each output node receives the net signal from the respective input node, and in response to the respective received net signal each output node selecting a category of the corresponding input node such that the output nodes provide a mapping between plural parts of the input pattern and plural categories, each output node providing an output signal indicative of category selection; a modulation mechanism coupled between the output nodes and the input nodes, for modifying category selections of the output nodes by modulating the input nodes with a modulation signal such that the sum of the output signals from the output nodes is within a predefined range, upon the sum of the output signals being within the predefined range, the output nodes providing categorization of the input pattern from the mapping between plural parts of the input pattern and plural categories of the input nodes; and for each weight of the input nodes, a learning mechanism for adapting the respective value of each weight to an exact analytical value in response to the modulation signal.