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A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive

Min Han¹, Jianchao Fan, Jun Wang

¹School of Electronic and Information Engineering, Dalian University of Technology, Dalian, China. minhan@dlut.edu.cn

IEEE Transactions on Neural Networks

|August 2, 2011

Summary

A novel dynamic feedforward neural network (DFNN) combined with Gaussian particle swarm optimization (GPSO) enhances predictive control for complex nonlinear systems. This approach improves accuracy and efficiency in identifying and controlling systems with long time delays.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Computational Science

Background:

Predictive control of nonlinear dynamic systems with long time delays presents significant challenges.
Existing methods often struggle with generalization and computational efficiency.

Purpose of the Study:

To propose a novel dynamic feedforward neural network (DFNN) integrated with Gaussian particle swarm optimization (GPSO) for enhanced predictive control.
To improve the generalization capability for nonlinear systems with long time delays.
To ensure computational efficiency and robust stability.

Main Methods:

Development of a DFNN incorporating adaptive time-delay operators.
Utilization of GPSO with a chaotic map and Gaussian function for parameter optimization.
Analysis of particle dynamics stability using robust stability theory.
Derivation of a stability condition for the GPSO+DFNN model.

Main Results:

The proposed GPSO+DFNN model demonstrates improved computational efficiency and performance compared to existing algorithms.
Adaptive particle velocity ranges enhance the optimization process.
The model effectively identifies and controls nonlinear systems with long time delays, validated by simulation results.

Conclusions:

The integrated GPSO+DFNN approach offers a robust and efficient solution for predictive control of complex nonlinear systems.
The method achieves satisfactory global search and rapid convergence without requiring gradient information.
The findings highlight the potential of this combined algorithm for real-world applications involving systems with long time delays.