US 5,835,901 A · Perceptive system including a neural network

Abstract

A real-time learning (RTL) neural network is capable of indicating when an input feature vector is novel with respect to feature vectors contained within its training data set, and is capable of learning to generate a correct response to a new data vector while maintaining correct responses to previously learned data vectors without requiring that the neural network be retrained on the previously learned data. The neural network has a sensor for inputting a feature vector, a first layer and a second layer. The feature vector is supplied to the first layer which may have one or more declared and unused nodes. During training, the input feature vector is clustered to a declared node only if it lies within a hypervolume defined by the declared node's automatically selectable reject radius, else the input feature vector is clustered to an unused node. Clustering in overlapping hypervolumes is determined by a decision surface. During testing of the RTL network, the same strategy is applied to cluster an input feature vector to declared (existing) nodes. If clustering occurs, then a classification signal corresponding to the node is generated. However, if the input feature vector is not clustered to a declared node, then the second layer outputs a signal indicating novelty. The RTL neural network is used in a perceptive system which alternatively selects the RTL network novelty output or the output of a classifier trained on historical target data if the input vector is a subset of the historical target data.

Claims27

Dependent claims

A neural network, comprising:

input means for receiving an input feature vector;
a first layer, coupled to said input means, comprising:
a first declared node, including:
a first feature vector;
and
a first hypervolume defined by said first feature vector and a first reject radius;
a second declared node, including:
a second feature vector;
and
a second hypervolume defined by said second feature vector and a second reject radius, wherein a portion of said second hypervolume overlaps a portion of said first hypervolume, thereby defining an overlapping region;
and
first means for clustering said input feature vector alternatively to only one of said first declared node and said second declared node if said input feature vector lies within said overlapping region.

9 more elements

A perceptive system, comprising:

input means for receiving an input feature vector;
a first classifier, coupled to said input means, for generating a first classification signal corresponding to said input feature vector, said first classifier generating said first classification signal with the use of a training data set that includes a target feature vector;
first means, coupled to said input means, for generating a first signal indicating whether or not said input feature vector is in a same general region of feature space as said training data set;
a second classifier comprising real-time learning means, coupled to said input means, for generating a real-time classification signal based on said input feature vector and a real-time data set;
and
selection means, coupled to said first classifier, said first means, and said real-time learning means, for using said first signal to select as a system output one of said first classification signal and said real-time classification signal.

3 more elements

A method for classifying feature vectors in a real-time learning neural network, said method comprising the steps of:

in a first layer of the real-time learning network that contains a non-adaptive node, clustering a feature vector to the non-adaptive node if the feature vector falls within a reject radius surrounding the non-adaptive node;
if the feature vector clusters to the non-adaptive node in the first layer, then generating a classification signal, wherein the classification signal indicates that the feature vector belongs to a class associated with the non-adaptive node;
if the feature vector fails to cluster to the non-adaptive node in the first layer of the real-time learning network, then supplying the feature vector to a second layer of the real-time learning network and performing the steps of:
clustering the feature vector to an adaptive node in the second layer of the real-time learning network if the feature vector falls within a reject radius surrounding the adaptive node;
generating a new adaptive node in the second layer based on the feature vector if the feature vector fails to cluster to the adaptive node;
selecting a reject radius for the new adaptive node from a number of stochastically generated reject radii;
converting the adaptive node to a new non-adaptive node and removing the new non-adaptive node from the second layer and adding the new non-adaptive node to the first layer, if the adaptive node has a statistically significant number of feature vectors clustered to it;
and
removing the adaptive node from the real-time learning network if it fails to cluster a statistically significant number of feature vectors,
wherein the feature vector can cluster to only one node in the real-time learning neural network.

7 more elements

Dependencies are inferred from the claim text.

Perceptive system including a neural network

Life of this patent

Perceptive system including a neural network

Life of this patent

Abstract

Claims27