Neural node network and model, and method of teaching same

Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

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Title: Neural node network and model, and method of teaching same

Abstract

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modelingmore » « less

Inventors:: Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

Issue Date:: Sun Jan 01 00:00:00 EST 1995

Research Org.:: Texas A&M Univ., College Station, TX (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 870229

Patent Number(s):: 5479571

Application Number:: 08/046,936

Assignee:: The Texas A&M University System (College Station, TX)

Patent Classifications (CPCs):: G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

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G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G06N3/049 - {Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs}

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DOE Contract Number:: FG07-89ER12893

Resource Type:: Patent

Resource Relation:: Patent File Date: 1993 Apr 13

Country of Publication:: United States

Language:: English

Subject:: /706/

Citation Formats


                    Parlos, Alexander G., Atiya, Amir F., Fernandez, Benito, Tsai, Wei K., and Chong, Kil T. Neural node network and model, and method of teaching same.  United States: N. p., 1995. 
        Web.

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                    Parlos, Alexander G., Atiya, Amir F., Fernandez, Benito, Tsai, Wei K., & Chong, Kil T. Neural node network and model, and method of teaching same.  United States.

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                    Parlos, Alexander G., Atiya, Amir F., Fernandez, Benito, Tsai, Wei K., and Chong, Kil T. Sun .  
        "Neural node network and model, and method of teaching same".  United States.  https://www.osti.gov/servlets/purl/870229.

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@article{osti_870229,

  title        = {Neural node network and model, and method of teaching same},

  author       = {Parlos, Alexander G. and Atiya, Amir F. and Fernandez, Benito and Tsai, Wei K. and Chong, Kil T.},

  abstractNote = {The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Jan 01 00:00:00 EST 1995},

  month        = {Sun Jan 01 00:00:00 EST 1995}

}

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Similar Records

Title: Neural node network and model, and method of teaching same

Abstract

Citation Formats

Neural networks and physical systems with emergent collective computational abilities. journal, April 1982

Generalization of back-propagation to recurrent neural networks journal, November 1987

Approximation by superpositions of a sigmoidal function journal, December 1989

Learning state space trajectories in recurrent neural networks conference, January 1989

Adaptation and tracking in system identification—A survey journal, January 1990

Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks journal, January 1991

Issues in system identification journal, January 1991

Complex temporal sequence learning based on short-term memory journal, January 1990

Self-organizing network for optimum supervised learning journal, March 1990

Structure identification of nonlinear dynamic systems—A survey on input/output approaches journal, July 1990

Chaotic dynamics of time-delay neural networks conference, January 1990

Neural networks for system identification journal, April 1990

Identification and control of dynamical systems using neural networks journal, March 1990

Nonlinear dynamic system identification using artificial neural networks (ANNs) conference, January 1990

Recurrent Backpropagation and the Dynamical Approach to Adaptive Neural Computation journal, June 1989

A comparison of the backpropagation and recursive prediction error algorithms for training neural networks journal, May 1991

An accelerated learning algorithm for multilayer perceptron networks journal, May 1994

A recurrent time-delay neural network for improved phoneme recognition conference, January 1991

Backpropagation through time: what it does and how to do it journal, January 1990

Principal component analysis by gradient descent on a constrained linear Hebbian cell conference, January 1989

Speaker-independent recognition of connected utterances using recurrent and non-recurrent neural networks conference, January 1989

Use of neural nets for dynamic modeling and control of chemical process systems journal, May 1990

Bounding the states of systems with unknown-but-bounded disturbances journal, October 1990

Identification of nonlinear systems–a survey journal, January 1980

Learning by parallel forward propagation conference, January 1990

A Learning Algorithm for Continually Running Fully Recurrent Neural Networks journal, June 1989

An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories journal, December 1990

Neural networks and physical systems with emergent collective computational abilities.
journal, April 1982

Generalization of back-propagation to recurrent neural networks
journal, November 1987

Approximation by superpositions of a sigmoidal function
journal, December 1989

Learning state space trajectories in recurrent neural networks
conference, January 1989

Adaptation and tracking in system identification—A survey
journal, January 1990

Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks
journal, January 1991

Issues in system identification
journal, January 1991

Complex temporal sequence learning based on short-term memory
journal, January 1990

Self-organizing network for optimum supervised learning
journal, March 1990

Structure identification of nonlinear dynamic systems—A survey on input/output approaches
journal, July 1990

Chaotic dynamics of time-delay neural networks
conference, January 1990

Neural networks for system identification
journal, April 1990

Identification and control of dynamical systems using neural networks
journal, March 1990

Nonlinear dynamic system identification using artificial neural networks (ANNs)
conference, January 1990

Recurrent Backpropagation and the Dynamical Approach to Adaptive Neural Computation
journal, June 1989

A comparison of the backpropagation and recursive prediction error algorithms for training neural networks
journal, May 1991

An accelerated learning algorithm for multilayer perceptron networks
journal, May 1994

A recurrent time-delay neural network for improved phoneme recognition
conference, January 1991

Backpropagation through time: what it does and how to do it
journal, January 1990

Principal component analysis by gradient descent on a constrained linear Hebbian cell
conference, January 1989

Speaker-independent recognition of connected utterances using recurrent and non-recurrent neural networks
conference, January 1989

Use of neural nets for dynamic modeling and control of chemical process systems
journal, May 1990

Bounding the states of systems with unknown-but-bounded disturbances
journal, October 1990

Identification of nonlinear systems–a survey
journal, January 1980

Learning by parallel forward propagation
conference, January 1990

A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
journal, June 1989

An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories
journal, December 1990