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Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

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Robust neural network tracking controller using simultaneous perturbation stochastic approximation.

Qing Song¹, James C Spall, Yeng Chai Soh

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. eqsong@ntu.edu.sg

IEEE Transactions on Neural Networks

|May 10, 2008

Summary

This summary is machine-generated.

This study introduces robust neural network controllers for nonlinear systems, ensuring stability and performance using conic sector theory and simultaneous perturbation stochastic approximation (SPSA) training.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Nonlinear Dynamics

Background:

Designing effective controllers for complex nonlinear systems remains a significant challenge.
Existing methods often struggle with robustness and stability guarantees.
Neural networks offer potential for approximating unknown system dynamics.

Purpose of the Study:

To develop a robust neural network tracking controller for nonlinear systems.
To guarantee the boundedness of signals and neural network weights.
To improve tracking performance and system robustness.

Main Methods:

Utilizing neural networks within a closed-loop system to estimate nonlinear system functions.
Applying conic sector theory to ensure system robustness and signal boundedness.
Employing the simultaneous perturbation stochastic approximation (SPSA) method for neural network training, replacing traditional backpropagation (BP).

Main Results:

The proposed neural control system demonstrates guaranteed closed-loop stability.
Achieved robust tracking performance with bounded input/output signals and neural network weights.
The SPSA method facilitated faster convergence and prevented weight shifts compared to BP.

Conclusions:

The conic sector-based robust neural control system effectively handles nonlinear systems.
SPSA training enhances convergence speed and robustness against disturbances.
This approach offers a stable and high-performance solution for neural network-based control.