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Recurrent neural network training with feedforward complexity.

¹Dept. of Electr. and Comput. Eng., George Mason Univ., Fairfax, VA.

IEEE Transactions on Neural Networks

|January 1, 1994

Summary

This study introduces an exact training method for recurrent neural networks (RNNs) with feedforward complexity. The approach transforms RNNs to reveal an embedded feedforward structure, simplifying training and parameter acquisition.

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Area of Science:

Artificial Intelligence
Computational Neuroscience
Dynamical Systems

Background:

Recurrent neural networks (RNNs) are powerful tools for modeling sequential data but are computationally intensive to train.
Existing training methods for RNNs often involve approximations or high computational complexity.

Purpose of the Study:

To present an exact training method for fully recurrent networks that achieves feedforward complexity.
To demonstrate a novel approach for simplifying the training of recurrent neural networks.

Main Methods:

The method relies on an exact transformation to reveal an embedded feedforward structure within any recurrent network.
Training is performed on this embedded feedforward structure using unambiguous training data, including state variables and their derivatives.
Recurrent network parameters are derived through an inverse transformation involving only linear operators.

Main Results:

The training method achieves feedforward complexity without approximation.
The approach successfully models a nonlinear dynamical system, learning Bessel's differential equation.
The model generates accurate Bessel functions both within and outside the training data range.

Conclusions:

This exact transformation method offers an efficient and accurate way to train recurrent neural networks.
The technique simplifies the training of complex dynamical systems, demonstrating broad applicability.