Method and apparatus for signal classification using a multilayer network

A method for classifying a signal representing spoken sounds using a multilayer temporal relaxation network comprising the steps of:

• receiving in a first layer, having a plurality of interconnected phoneme nodes with associated activation values, an input signal feature vector representing a phoneme included in said spoken sounds;
• applying a multilayer relaxation process for a current time step, wherein the multilayer relaxation process updates selected ones of the activation values for the phoneme nodes and updates selected ones of activation values associated with a plurality of word nodes in a second layer;
• providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• and
• outputting a word in the current time step.

A method for classifying an image of handwritten text using a multilayer temporal relaxation network comprising the steps of:

• receiving in a first layer an input image feature vector;
• applying a multilayer relaxation process that updates an activation value of a node in the multilayer network for a current spatial scan interval;
• determining whether there is a candidate classification of the image of handwritten text for the current spatial scan interval in a second layer;
• providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• outputting the candidate classification for the current spatial scan interval;
• and
• incrementing the current spatial scan interval.

A method for forecasting weather using a multilayer temporal relaxation network comprising the steps of:

• receiving in a first layer an input reading corresponding to a weather variable;
• updating an activation value of a node in the multilayer network for a current time or spatial interval using multilayer relaxation;
• providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a spatial scan interval;
• determining whether there is a candidate weather forecast for the current time or spatial interval in a second layer;
• outputting the candidate weather forecast for the current time or spatial interval;
• and
• incrementing the current time or spatial interval.

A method for identifying an image using a multilayer temporal relaxation network comprising the steps of:

• receiving in a first layer an input pixel from said image;
• updating an activation value of a node in the multilayer network for a current spatial scan interval using multilayer relaxation;
• providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• determining whether there is a candidate identification for the current spatial scan interval in a second layer;
• outputting the candidate identification for the current spatial scan interval;
• and
• incrementing the current spatial scan interval.

A method for controlling a process using a multilayer temporal relaxation network comprising the steps of:

• receiving in a first layer an input reading corresponding to a process control parameter;
• updating an activation value of a node in the multilayer network for a current time interval using multilayer relaxation;
• determining whether there is a candidate control level for the current time interval in a second layer;
• providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• outputting the candidate control level for the current time interval;
• and
• incrementing the current time interval.

A method for classifying a signal using a multilayer relaxation network comprising the steps of:

• 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 f(t), where f(t)=k0(t+1)−1, when t<−1;
• −k1L((−t/k2)−2)+k3, when −1≦t<0;
• 0 when t=0;
• k1L((t/k2)−2)−k3, when 0<t≦1;
• and k0(t−1)+1, when t>1;
• wherein operator L(t)=1/(1+e−2t) and k0, k1, k2, and k3 are scaling constants;
• 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;
• and
• incrementing the current time step.

A method for classifying a signal using a multilayer relaxation network comprising the steps of:

• 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;
• and
• incrementing the current time step.

A multilayer temporal relaxation network apparatus for signal classification comprising:

• an input layer, having a first plurality of nodes with associated activation values, for receiving an input signal feature vector representing an input signal and for classifying at least part of said input signal according to a first signal feature;
• a context layer, having a second plurality of nodes with associated activation values, for classifying at least part of said input signal according to a second signal feature, the second signal feature representing a context of said input signal;
• means for providing interaction between said input layer and said context layer to update selected ones of the activation values for the nodes in said input layer and said context layer during classification, wherein said means for providing interaction includes relaxation means for updating said selected ones of the activation values through a controlled sequence of partial updates;
• means for providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• and
• means for generating a signal classification for said input signal from said input layer or said second context layer.

A multilayer temporal relaxation network apparatus for classifying a signal representing spoken sounds comprising:

• a first layer, having a plurality of phoneme nodes with associated activation values, for receiving an input signal feature vector representing an input signal associated with the spoken sounds and for classifying at least a portion of said spoken sounds as one of a plurality of possible phonemes;
• a second layer, having a plurality of word nodes with associated activation values, for classifying said spoken sounds as one of a plurality of possible words;
• means for providing multilayer relaxation interaction between said first and second layers to update selected ones of the activation values for phoneme nodes and word nodes;
• means for providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• and
• means for classifying said spoken sounds according to selected ones of the activation values in said first layer or according to selected ones of the activation values in said second layer.

A multilayer temporal relaxation network apparatus for classifying a signal representing spoken sounds comprising:

• a first layer of interconnected phoneme nodes, wherein each phoneme node has an activation value responsive to an input signal feature vector;
• a second layer of interconnected word nodes connected to said phoneme nodes of said first layer, wherein each word node has an activation value responsive to a sequence of active phoneme nodes;
• and
• means for providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time.

An apparatus for classifying a signal using a multilayer temporal relaxation network comprising:

• means for receiving an input signal feature vector;
• means for applying a multilayer relaxation process that updates an activation value of a node in the multilayer network for a current time step;
• means for providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• and
• means for outputting a classification.

A multilayer relaxation apparatus for classifying a signal comprising:

• a first multilayer network representing a first time step, said first multilayer network including a first layer and a second layer;
• a second multilayer network representing a second time step, said second multilayer network including a first layer and a second layer;
• means for relaxing said first multilayer network connecting said first and second layers of said first multilayer network;
• means for relaxing said second multilayer network connecting said first and second layers of said second multilayer network;
• means for relaxing said first and second multilayer networks together;
• and
• means for classifying said signal after relaxing said first and second multilayer networks.

A multilayer relaxation apparatus for classifying a signal comprising:

• a first multilayer neural network representing a first time step, said first multilayer neural network including a first neural network layer and a second neural network layer;
• a second multilayer neural network representing a second time step, said second multilayer neural network including a first neural network layer and a second neural network layer;
• means for relaxing said first multilayer neural network connecting said first and second neural network layers of said first multilayer neural network;
• means for relaxing said second multilayer neural network connecting said first and second neural network layers of said second multilayer neural network;
• means for relaxing said first and second multilayer neural networks together;
• and
• means for classifying said signal after relaxing said first and second multilayer neural networks.

A multilayer temporal relaxation network apparatus for classifying a signal representing spoken sounds comprising:

• a first layer having a plurality of phoneme nodes with associated activation values, for receiving an input signal feature vector representing an input signal associated with the spoken sounds and for classifying at least a portion of said spoken sounds as one of a plurality of possible phonemes;
• a second layer having a plurality of word nodes with associated activation values, for classifying said spoken sounds;
• means for providing multilayer relaxation interaction between said first and second layers to update selected ones of the activation values for phoneme nodes and word nodes;
• a third layer having a plurality of grammar nodes with associated activation values, for associating a grammatical part of speech with said spoken sounds classified by said second layer;
• means for providing multilayer relaxation interaction between said second and third layers to update selected ones of the word node activation values and selected ones of the grammar node activation values;
• means for providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• means for classifying said spoken sounds according to selected ones of the activation values of the phoneme nodes, word nodes, or grammar nodes;
• and
• a phoneme match node connecting said first layer and said second layer.

A multilayer temporal relaxation network apparatus for signal classification comprising:

• an input layer, having a first plurality of nodes with associated activation values, for receiving an input signal feature vector representing an input signal and for classifying at least part of said input signal according to a first signal feature;
• a context layer, having a second plurality of nodes with associated activation values, for classifying at least part of said input signal according to a second signal feature, the second signal feature representing a context of said input signal;
• means for providing multilayer relaxation interaction between said input layer and said context layer to update selected ones of the activation values for the nodes in said input layer and said context layer during classification;
• and
• means for providing interaction between multilayer relaxation networks, wherein each multilayer relaxation network represents a step in time;
• and
• means for generating a signal classification for said input signal from said input layer or said second context layer.

A multilayer relaxation network apparatus for signal classification comprising:

• an input layer, having a first plurality of nodes with associated activation values, for receiving an input signal feature vector representing an input signal and for classifying at least part of said input signal according to a first signal feature;
• a context layer, having a second plurality of nodes with associated activation values, for classifying at least part of said input signal according to a second signal feature, the second signal feature representing a context of said input signal, wherein said input layer or said context layer includes a node with an activation function f(t), where f(t)=k0(t+1)−1, when t<−1;
• −k1L((−t/k2)−2)+k3, when −1≦t<0;
• 0 when t=0;
• k1L((t/k2)−2)−k3, when 0<t≦1;
• and k0(t−1)+1, when t>1;
• wherein operator L(t)=1/(1+e−2t) and k0, k1, k2, and k3 are scaling constants;
• means for providing interaction between said input layer and said context layer to update selected ones of the activation values for the nodes in said input layer and said context layer during classification;
• and
• means for generating a signal classification for said input signal from said input layer or said second context layer.

A multilayer relaxation network apparatus for signal classification comprising:

• an input layer, having a first plurality of nodes with associated activation values, for receiving an input signal feature vector representing an input signal and for classifying at least part of said input signal according to a first signal feature;
• a context layer, having a second plurality of nodes with associated activation values, for classifying at least part of said input signal according to a second signal feature, the second signal feature representing a context of said input signal, wherein said input layer or said context layer includes a node with 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;
• means for providing interaction between said input layer and said context layer to update selected ones of the activation values for the nodes in said input layer and said context layer during classification;
• and
• means for generating a signal classification for said input signal from said input layer or said second context layer.

A computer program product for use in a computer adapted for classifying a signal, the computer program product comprising a computer readable medium for storing computer readable code means, which when executed by a the computer, enables the computer to classify a signal, and wherein the computer readable code means includes computer readable instructions for causing the computer to execute a method comprising the steps of:

• receiving an input signal feature vector in a first layer of a multilayer network;
• updating an activation value of a node in the multilayer network for a current time step using multilayer relaxation;
• 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;
• and
• incrementing the current time step.