Non-Linear Dynamic Models

1989 ◽  
pp. 511-546
Author(s):  
Mike West ◽  
Jeff Harrison
Keyword(s):  
Dynamic Models ◽  
Linear Dynamic ◽  
Non Linear ◽  
Linear Dynamic Models

scholarly journals Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

2011 ◽  
Vol 5 (1) ◽  
pp. 137 ◽  
Author(s):  
Carlos Pozo ◽  
Alberto Marín-Sanguino ◽  
Rui Alves ◽  
Gonzalo Guillén-Gosálbez ◽  
Laureano Jiménez ◽  
...  
Keyword(s):  
Global Optimization ◽  
Steady State ◽  
Power Law ◽  
Dynamic Models ◽  
Canonical Models ◽  
Linear Dynamic ◽  
Non Linear ◽  
Linear Dynamic Models

scholarly journals Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 510 ◽  
Author(s):  
Longlong Liu ◽  
Di Ma ◽  
Ahmad Taher Azar ◽  
Quanmin Zhu
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Linear Model ◽  
Dynamic Models ◽  
Bench Test ◽  
Model Parameters ◽  
Linear Dynamic ◽  
Gradient Descent Algorithm ◽  
Non Linear ◽  
Linear Dynamic Models

In this paper, a gradient descent algorithm is proposed for the parameter estimation of multi-input and multi-output (MIMO) total non-linear dynamic models. Firstly, the MIMO total non-linear model is mapped to a non-completely connected feedforward neural network, that is, the parameters of the total non-linear model are mapped to the connection weights of the neural network. Then, based on the minimization of network error, a weight-updating algorithm, that is, an estimation algorithm of model parameters, is proposed with the convergence conditions of a non-completely connected feedforward network. In further determining the variables of the model set, a method of model structure detection is proposed for selecting a group of important items from the whole variable candidate set. In order to verify the usefulness of the parameter identification process, we provide a virtual bench test example for the numerical analysis and user-friendly instructions for potential applications.

Efficient bayesian learning in non-linear dynamic models

1990 ◽  
Vol 9 (2) ◽  
pp. 119-136 ◽  
Author(s):  
Andy Pole ◽  
Mike West
Keyword(s):  
Bayesian Learning ◽  
Dynamic Models ◽  
Linear Dynamic ◽  
Non Linear ◽  
Linear Dynamic Models
Sign in / Sign up

Export Citation Format

Share Document