Recurrent neural networks for synthesizing linear control systems via pole placement

DOI10.1080/00207729508929174zbMath0850.93319OpenAlexW1972885637MaRDI QIDQ4862283

Jun Wang, Guang Wu

Publication date: 17 July 1996

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207729508929174

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Pole and zero placement problems (93B55)

Related Items

Robust eigenvalue assignment in second-order systems: a gradient flow approach, A multilayer recurrent neural network for on-line synthesis of minimum-norm linear feedback control systems via pole assignment, Properties and computation of continuous-time solutions to linear systems, A new approach for instantaneous pole placement with recurrent neural networks and its application in control of nonlinear time-varying systems

Cites Work

• Pole assignment via Sylvester's equation
• Neural network approach to computing matrix inversion
• Recurrent neural networks for LU decomposition and Cholesky factorization
• Solving simultaneous linear equations using recurrent neural networks
• Neural net based model predictive control
• Neural networks for solving systems of linear equations and related problems
• Stable control of nonlinear systems using neural networks
• Neural-net computing and the intelligent control of systems
• An adaptive control for CARMA systems using linear neural networks
• Solutions of the equation AV+BW=VF and their application to eigenstructure assignment in linear systems