The main important thing about modelling a system is to understand the behaviour and to aid in designing a system (Soderstrom and Stoica, 1989). There are several steps to be considered in constructing and identifying a system's behaviour (Ljung, 1999). The first step is data acquisition which is to gather input and output data of the system to be identified either from experiment data through real process plant or simulated data. Then, a model structure consists of mathematical equation that describes the behaviour of a system is chosen to represent the system. The next step is to estimate the parameters of the model structure to complete the mathematical representation of the system. Finally, model validation is done to verify w...
Model structure selection is a problem in system identification which addresses selecting an adequat...
A method for identifying the structure of non-linear polynomial dynamic models is presented. This ap...
Typically, in the automotive field, classic estimation or filtering techniques (e.g. Least Squares, ...
The main important thing about modelling a system is to understand the behaviour and to aid in desig...
The development of a multivariable system identification model for dynamic discrete-time nonlinear s...
This paper demonstrates the ability of Genetic Algorithms (GAs) in the identification of dynamical n...
Genetic Programming (GP) is a powerful nonlinear optimisation tool which can be applied to the ident...
Genetic Programming is an optimisation procedure which may be applied to the identification of the n...
Parameter estimation is the process of using observations from a system to develop mathematical mode...
In recent years, extensive works on genetic algorithms have been reported covering various applicati...
In recent years, extensive works on genetic algorithms have been reported covering various applicati...
The purpose of this study is to investigate the application of genetic algorithm (GA) in modelling l...
Abstract Macroscopic models are useful for example in process control and optimization. They are bas...
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-dri...
This paper develops a genetic algorithm based technique that may be used to identify multivariable s...
Model structure selection is a problem in system identification which addresses selecting an adequat...
A method for identifying the structure of non-linear polynomial dynamic models is presented. This ap...
Typically, in the automotive field, classic estimation or filtering techniques (e.g. Least Squares, ...
The main important thing about modelling a system is to understand the behaviour and to aid in desig...
The development of a multivariable system identification model for dynamic discrete-time nonlinear s...
This paper demonstrates the ability of Genetic Algorithms (GAs) in the identification of dynamical n...
Genetic Programming (GP) is a powerful nonlinear optimisation tool which can be applied to the ident...
Genetic Programming is an optimisation procedure which may be applied to the identification of the n...
Parameter estimation is the process of using observations from a system to develop mathematical mode...
In recent years, extensive works on genetic algorithms have been reported covering various applicati...
In recent years, extensive works on genetic algorithms have been reported covering various applicati...
The purpose of this study is to investigate the application of genetic algorithm (GA) in modelling l...
Abstract Macroscopic models are useful for example in process control and optimization. They are bas...
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-dri...
This paper develops a genetic algorithm based technique that may be used to identify multivariable s...
Model structure selection is a problem in system identification which addresses selecting an adequat...
A method for identifying the structure of non-linear polynomial dynamic models is presented. This ap...
Typically, in the automotive field, classic estimation or filtering techniques (e.g. Least Squares, ...