Add to ORKGA method for identifying the structure of non-linear polynomial dynamic models is presented. This approach uses an evolutionary algorithm, Genetic Programming, in a multiobjective fashion to generate global models which describe the dynamic behaviour of the non-linear system under investigation. The introduction of the validation stage of system identification into the multiobjective tool is also explored, in order to direct the identification process to a set of global models of the system
An alogrithm for the identification of non-linear systems which can be described by a model consisti...
This paper demonstrates the ability of Genetic Algorithms (GAs) in the identification of dynamical n...
In the general method analysis applied to any nonlinear system depends to a large extent on the stru...
This paper points out how combined Genetic Programming techniques can be applied to the identificati...
Genetic programming (GP) is applied to a multobjective optimisation problem and the advantages of ...
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...
The nonlinear systems identification method described in the paper is based on genetic programming, ...
A new nonlinear rational model identification algorithm is introduced based on genetic algorithms. ...
A new algorithm which preselects variables in nonlinear system models is introduced by converting t...
\u3cp\u3eState-of-the-art methods for data-driven modelling of non-linear dynamical systems typicall...
Genetic programming can be used for structural optimisation. Combined with a hybrid simplex/simulate...
Linear in parameter models are quite widespread in process engineering, e.g. NAARX, polynomial ARMA ...
Abstract. This paper introduces a Multi-Branch Genetic Programming (MB-GP) encoding applied for mode...
The main important thing about modelling a system is to understand the behaviour and to aid in desig...
An alogrithm for the identification of non-linear systems which can be described by a model consisti...
This paper demonstrates the ability of Genetic Algorithms (GAs) in the identification of dynamical n...
In the general method analysis applied to any nonlinear system depends to a large extent on the stru...
This paper points out how combined Genetic Programming techniques can be applied to the identificati...
Genetic programming (GP) is applied to a multobjective optimisation problem and the advantages of ...
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...
The nonlinear systems identification method described in the paper is based on genetic programming, ...
A new nonlinear rational model identification algorithm is introduced based on genetic algorithms. ...
A new algorithm which preselects variables in nonlinear system models is introduced by converting t...
\u3cp\u3eState-of-the-art methods for data-driven modelling of non-linear dynamical systems typicall...
Genetic programming can be used for structural optimisation. Combined with a hybrid simplex/simulate...
Linear in parameter models are quite widespread in process engineering, e.g. NAARX, polynomial ARMA ...
Abstract. This paper introduces a Multi-Branch Genetic Programming (MB-GP) encoding applied for mode...
The main important thing about modelling a system is to understand the behaviour and to aid in desig...
An alogrithm for the identification of non-linear systems which can be described by a model consisti...
This paper demonstrates the ability of Genetic Algorithms (GAs) in the identification of dynamical n...
In the general method analysis applied to any nonlinear system depends to a large extent on the stru...