초록 ▼

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 previ

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.

대표청구항 ▼

[ What is claimed is:] [1.] 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; anda first hypervolume defined by said first feature vector and a first reject ra

[ What is claimed is:] [1.] 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; anda first hypervolume defined by said first feature vector and a first reject radius;a second declared node, including:a second feature vector; anda 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; andfirst 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.