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Diagonal recurrent neural networks for dynamic systems control.

C C Ku¹, K Y Lee

¹Dept. of Electr. and Comput. Eng., Pennsylvania State Univ., University Park, PA.

IEEE Transactions on Neural Networks

|January 1, 1995

Summary

This summary is machine-generated.

A novel diagonal recurrent neural network (DRNN) architecture is introduced for control systems. This new model, along with adaptive learning rates, enhances system identification and control performance.

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Area of Science:

Artificial Intelligence
Machine Learning
Control Systems Engineering

Background:

Recurrent neural networks (RNNs) are effective for dynamic systems but can be computationally intensive.
Existing RNN architectures may face challenges in efficiently capturing complex system dynamics.

Purpose of the Study:

To introduce a new neural network architecture, the diagonal recurrent neural network (DRNN).
To develop and apply DRNNs within a control system framework for system identification and control.
To enhance training efficiency and convergence using adaptive learning rates and Lyapunov functions.

Main Methods:

The diagonal recurrent neural network (DRNN) architecture is proposed, modifying fully connected RNNs with self-recurrent neurons in the hidden layer.
Two DRNN models, diagonal recurrent neuroidentifier (DRNI) and diagonal recurrent neurocontroller (DRNC), are developed for a control system.
A generalized dynamic backpropagation (DBP) algorithm with adaptive learning rates, incorporating a Lyapunov function, is used for training.

Main Results:

The DRNN architecture effectively captures the dynamic behavior of systems due to its recurrent nature.
Convergence theorems for adaptive backpropagation algorithms are established for both DRNI and DRNC.
Simulation results on numerical problems demonstrate the efficacy of the proposed DRNN paradigm.

Conclusions:

The diagonal recurrent neural network (DRNN) presents a promising new paradigm for advanced control systems.
The developed adaptive learning rate approach ensures convergence and accelerates training for DRNN models.
The DRNN, DRNI, and DRNC show significant potential for applications in complex dynamic system control and identification.