Intelligent control with hierarchical stacked neural networks
First Claim
1. A system configured to analyzing at least one data pattern comprising:
- an input configured to receive at least one data pattern;
at least one hierarchical neural network, having a plurality of hierarchical layers, each respective hierarchical layer being configured to receive a respective input and to produce a non-arbitrary organization of actions in dependence on the respective input and a respective layer training, the at least one hierarchical neural network comprising;
a first layer configured to produce a non-arbitrary organization of actions which identifies at least one data object from a plurality of data objects, based at least the first layer training to identify a plurality of different data objects, and the at least one data pattern, and to produce a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the at least one data pattern from a prototype of the data object identified,a second layer, configured to receive the respective non-arbitrary organization of actions from the first layer identifying the object as the respective input, based on the non-arbitrary organization of actions from the first layer, to ascertain a type of the identified data object from a plurality of different types of each of the plurality of different data objects;
wherein the first layer further produces a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the at least one data pattern from a prototype of the data object identified, anda processor configured to at least one of;
determine a confidence of data object identification, and determine that the data pattern comprises a data object not properly identified by the first layer.
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Abstract
A system and method of detecting an aberrant message is provided. An ordered set of words within the message is detected. The set of words found within the message is linked to a corresponding set of expected words, the set of expected words having semantic attributes. A set of grammatical structures represented in the message is detected, based on the ordered set of words and the semantic attributes of the corresponding set of expected words. A cognitive noise vector comprising a quantitative measure of a deviation between grammatical structures represented in the message and an expected measure of grammatical structures for a message of the type is then determined. The cognitive noise vector may be processed by higher levels of the neural network and/or an external processor.
711 Citations
20 Claims
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1. A system configured to analyzing at least one data pattern comprising:
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an input configured to receive at least one data pattern; at least one hierarchical neural network, having a plurality of hierarchical layers, each respective hierarchical layer being configured to receive a respective input and to produce a non-arbitrary organization of actions in dependence on the respective input and a respective layer training, the at least one hierarchical neural network comprising; a first layer configured to produce a non-arbitrary organization of actions which identifies at least one data object from a plurality of data objects, based at least the first layer training to identify a plurality of different data objects, and the at least one data pattern, and to produce a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the at least one data pattern from a prototype of the data object identified, a second layer, configured to receive the respective non-arbitrary organization of actions from the first layer identifying the object as the respective input, based on the non-arbitrary organization of actions from the first layer, to ascertain a type of the identified data object from a plurality of different types of each of the plurality of different data objects; wherein the first layer further produces a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the at least one data pattern from a prototype of the data object identified, and a processor configured to at least one of;
determine a confidence of data object identification, and determine that the data pattern comprises a data object not properly identified by the first layer. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for analyzing at least one data pattern with at least one hierarchical neural network, having a plurality of layers, each layer being configured to receive a respective input and to produce a non-arbitrary organization of actions in dependence on the respective input and a respective layer training, the at least one hierarchical neural network comprising:
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a first layer configured to identify at least one data object in at least one data pattern presented at the respective input, from a plurality of different data objects, based on at least the first layer training and the at least one data pattern, and to produce a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the at least one data pattern from a prototype of the data object identified, a second layer, configured to receive the respective non-arbitrary organization of actions from the first layer identifying the data object as the respective input, and, based on the respective layer training of the second layer and the respective non-arbitrary organization of actions from the first layer, to ascertain a type of the identified data object from a plurality of different types of each of the plurality of different data objects; and a processor configured to at least one of determine a confidence of a data object identification, and determine that the data pattern comprises a data object not properly identified by the first layer, the method comprising; receiving the at least one data pattern at a lowest hierarchical layer; processing the at least one data patter to identify the data object; producing the noise vector representing the deviance of at least a portion of the at least one data pattern from a prototype of the data object identified; processing the non-arbitrary organization of actions from the first layer with the second layer, to ascertain the type of identified object; and determining with the processor at least one of the confidence of a data object identification, and that the data pattern comprises a data object not properly identified by the first layer. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method for analyzing at least one data pattern, with at least two layer of an interconnected hierarchical neural network, each layer receiving a respective input and producing a non-arbitrary organization of actions in dependence on a respective layer input and a respective layer training, comprising:
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providing a first layer of the interconnected hierarchical neural network, which identifies a data object represented in a data pattern presented at the respective first layer input from a plurality of different data objects which form a basis for the first layer training, and produce a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the data pattern from a prototype of the data object identified; providing a second layer of the interconnected hierarchical neural network, which receives the respective non-arbitrary organization of actions from the first layer as the respective second layer input, and classifies the identified data object from a plurality of different types of the identified data object from a plurality of different data object types which form a basis for the second layer training; and determining a confidence of the identification of the data object by the first layer based on at least the non-arbitrary organization of actions of the first layer and the noise vector output.
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Specification