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A Penalty Strategy Combined Varying-Parameter Recurrent Neural Network for Solving Time-Varying Multi-Type

Zhijun Zhang, Song Yang, Lunan Zheng

IEEE Transactions on Neural Networks and Learning Systems

|July 30, 2020

Summary

A new penalty strategy combined varying-parameter recurrent neural network (PS-VP-RNN) effectively solves time-varying quadratic programming (TVQP) problems. This method handles equality, inequality, and bounded constraints, demonstrating accuracy and effectiveness in simulations.

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Area of Science:

Optimization
Machine Learning
Control Theory

Background:

Time-varying quadratic programming (TVQP) problems present significant computational challenges.
Existing methods struggle with complex constraints, including inequalities and bounded conditions.

Purpose of the Study:

To propose and analyze a novel penalty strategy combined varying-parameter recurrent neural network (PS-VP-RNN) for solving TVQP problems.
To address TVQP problems with equality and multitype inequality constraints.

Main Methods:

A novel penalty function transforms inequality constraints into a penalty term within the objective function.
A varying-parameter recurrent neural network (VP-RNN) is designed to incorporate this penalty term for TVQP.
The global convergence of the proposed PS-VP-RNN is theoretically proven.

Main Results:

The PS-VP-RNN successfully solves TVQP problems with equality constraints.
The method demonstrates capability in handling inequality and bounded constraints.
Numerical simulations confirm the effectiveness and accuracy of the PS-VP-RNN.

Conclusions:

The PS-VP-RNN offers a robust and accurate approach for solving complex TVQP problems.
This method provides a unified framework for TVQP with various constraint types.
The validated effectiveness opens avenues for applications in dynamic optimization and control systems.