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Updated: Jul 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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A neural network regulator for turbogenerators.

Q H Wu¹, B W Hogg, G W Irwin

¹Dept. of Electr. Eng., Queen's Univ. of Belfast.

IEEE Transactions on Neural Networks

|January 1, 1992

Summary

This summary is machine-generated.

A novel neural network (NN) regulator offers improved control for complex turbogenerator systems. This innovative approach enhances performance without needing a reference or inverse model, demonstrating significant potential.

Related Experiment Videos

Last Updated: Jul 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Engineering
Control Systems
Artificial Intelligence

Background:

Turbogenerator systems require sophisticated control for stability and efficiency.
Existing adaptive controllers may have limitations in handling nonlinear, multivariable dynamics.

Purpose of the Study:

To present a neural network (NN)-based regulator for nonlinear, multivariable turbogenerator control.
To design a flexible NN regulator architecture capable of handling multi-input, multi-output systems.

Main Methods:

A hierarchical neural network architecture was proposed, comprising two subnetworks for input-output mapping and control.
The back-propagation (BP) algorithm was utilized for training the neural network subnetworks.
The NN regulator was implemented and tested on a complex turbogenerator system model.

Main Results:

The NN regulator demonstrated satisfactory control performance in simulations.
The proposed NN regulator produced more acceptable control signals compared to methods using the sign of plant errors.
The NN regulator's operation did not require a reference model or an inverse system model.

Conclusions:

The developed NN regulator shows significant potential for advanced turbogenerator control.
The NN regulator offers a flexible and effective alternative to existing adaptive control methods.
The hierarchical NN architecture is well-suited for complex, multivariable control applications.