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The dependence identification neural network construction algorithm.

J O Moody¹, P J Antsaklis

¹Dept. of Electr. Eng., Notre Dame Univ., IN.

IEEE Transactions on Neural Networks

|January 1, 1996

Summary

This study introduces dependence identification, an algorithm for building and training multilayer neural networks. It simplifies network training and speeds up development by solving optimization problems and refining architectures.

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Area of Science:

Artificial Intelligence
Machine Learning
Neural Networks

Background:

Multilayer neural networks require efficient construction and training methods.
Current methods often involve extensive trial and error.
Optimizing network architecture and weights is crucial for performance.

Purpose of the Study:

To present a novel algorithm, dependence identification, for constructing and training multilayer neural networks.
To address the challenges of network development time and trial-and-error processes.
To offer a method that integrates with existing training algorithms for further refinement.

Main Methods:

Transforms neural network training into solvable quadratic optimization problems.
Utilizes linear equations to solve these optimization problems.

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Constructs a network architecture tailored to specific training requirements.

Main Results:

The algorithm transforms the training problem into a set of quadratic optimization problems.
It constructs a suitable network architecture based on training specifications.
The developed network architecture and weights can be refined using standard algorithms like backpropagation.

Conclusions:

Dependence identification offers a streamlined approach to neural network construction and training.
This method significantly reduces development time and minimizes trial-and-error.
The algorithm facilitates faster and more efficient neural network development.