Nonlinear dynamic system modeling based on neural state space model

2001 ◽  
Author(s):  
Yongji Wang ◽  
Qing Wu ◽  
Hong Wang
Keyword(s):  
Dynamic System ◽  
State Space ◽  
Nonlinear Dynamic ◽  
System Modeling ◽  
State Space Model ◽  
Nonlinear Dynamic System ◽  
Space Model ◽  
Dynamic System Modeling

Recursive Immuno-Inspired Algorithm for Time Variant Discrete Multivariable Dynamic System State Space Identification

2015 ◽  
Vol 5 (2) ◽  
pp. 69-100 ◽  
Author(s):  
Mateus Giesbrecht ◽  
Celso Pascoli Bottura
Keyword(s):  
Dynamic System ◽  
State Space ◽  
System Modeling ◽  
State Space Model ◽  
Small Time ◽  
Time Interval ◽  
System State ◽  
Space Model ◽  
Starting Point ◽  
Benchmark System

In this paper a recursive immuno inspired algorithm is proposed to identify time variant discrete multivariable dynamic systems. The main contribution of this paper has as starting point the idea that a multivariable dynamic system state space model can be seen as a point in a space defined by all possible matrices quadruples that define a state space model. With this in mind, the time variant discrete multivariable dynamic system modeling is transformed in an optimization problem and this problem is solved with an immuno inspired algorithm. To do that the inputs given to the system and the resulting outputs are divided in small sets containing data from small time intervals. These sets are defined as time windows, and for each window an immuno inspired optimization algorithm is applied to find the state space model that better represents the system at that time interval. The initial candidate solutions of each time interval are the ones of the last interval. The immuno inspired algorithm proposed in this paper has some modifications to the original Opt-AINet algorithm to deal with the constraints that are natural from the system identification problem and these modifications are also contributions of this paper. The method proposed in this paper was applied to identify a time variant benchmark system, resulting in a time variant model. The outputs estimated with this model are closer to the benchmark system outputs than the outputs estimated with models obtained by other known identification methods. The Markov parameters of the variant benchmark system are also reproduced by the time variant model found with the new method.

scholarly journals Dynamic system-optimal traffic assignment using a state space model

1993 ◽  
Vol 27 (6) ◽  
pp. 451-472 ◽  
Author(s):  
Stéphane Lafortune ◽  
Raja Sengupta ◽  
David E. Kaufman ◽  
Robert L. Smith
Keyword(s):  
Dynamic System ◽  
State Space ◽  
Traffic Assignment ◽  
State Space Model ◽  
Space Model ◽  
System Optimal

PERTURBATION PARAMETERS TUNING OF MULTI-OBJECTIVE OPTIMIZATION DIFFERENTIAL EVOLUTION AND ITS APPLICATION TO DYNAMIC SYSTEM MODELING

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
Hishamuddin Jamaluddin ◽  
Robiah Ahmad ◽  
Azmi Harun ◽  
Radhwan Hussin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Dynamic System ◽  
Dynamic Systems ◽  
System Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Structure Selection ◽  
Dynamic System Modeling ◽  
Perturbation Parameters ◽  
Model Structure Selection

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.

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