초록 ▼

A method and apparatus for signal classification using a multilayer temporal relaxation network involves receiving an input signal feature vector, classifying a first signal feature, and classifying a second signal feature using contextual information. The multilayer temporal relaxation network appl

A method and apparatus for signal classification using a multilayer temporal relaxation network involves receiving an input signal feature vector, classifying a first signal feature, and classifying a second signal feature using contextual information. The multilayer temporal relaxation network applies a relaxation process that updates an activation value of a node in a first layer and updates an activation value of a node in a second layer. The multilayer network then generates a signal classification according to an activation value of a node in the multilayer network.

대표청구항 ▼

[We claim:] [1.]receiving an input signal feature vector in a first layer;applying a multilayer relaxation process that updates an activation value of a node in the multilayer network for a current time step;providing interaction between multilayer relaxation networks, wherein each multilayer relaxa

[We claim:] [1.]receiving an input signal feature vector in a first layer;applying a multilayer relaxation process that updates an activation value of a node in the multilayer network for a current time step;providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;determining whether there is a candidate classification for the current time step in a second layer;outputting the candidate classification for the current time step; andincrementing the current time step. [19.]receiving an input signal feature vector in a first layer;applying a relaxation process that updates an activation value of a node in the multilayer network for a current time step, wherein the step of applying a relaxation process includes the substep of computing an activation value according to an activation function representing scaled and translated logistic functions combined to produce a flat response near the middle of the activation function domain and a linear response in a clamping region;determining whether there is a candidate classification for the current time step in a second layer;outputting the candidate classification for the current time step; andincrementing the current time step.