Nonlinear system identification using a Bayesian–Gaussian neural network for predictive control
✍ Scribed by Haiwen Ye; Weidou Ni
- Book ID
- 114297035
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 332 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0925-2312
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
The ability of a neural network to realize some complex nonlinear function makes them attractive for system identification. In the recent past, neural networks trained with backpropagation learning algorithm have gained attention for the identification of nonlinear dynamic systems. However, the conv
In this paper, optimal control for stochastic nonlinear singular system with quadratic performance is obtained using neural networks. The goal is to provide optimal control with reduced calculus effort by comparing the solutions of the matrix Riccati differential equation (MRDE) obtained from the we
The paper describes the design of a neural network based model predictive controller for controlling the interface level in a flotation column. For the system identification, the tailings valve opening is subjected to a pseudo-random ternary signal and response of the interface level is recorded ove