Using Additive Expression Programming for System Identification

The system identification is crucially important process, which could develop the mathematical representation of physical system from observed data. In this paper, a new model, called additive expression tree (AET) model is proposed to encode the linear and nonlinear systems. A new structure-based evolutionary algorithm and artificial bee colony (ABC) are used to optimize the architecture and parameters of additive expression tree model, respectively. Experimental results demonstrate that our proposed model and hybrid approach could identify the linear/nonlinear systems effectively.

[1]  Peter E. Wellstead.  Non-parametric methods of system identification , 1981, Autom..

[2]  Kumar Chellapilla,et al.  Evolving computer programs without subtree crossover , 1997, IEEE Trans. Evol. Comput..

[3]  Neil Gershenfeld,et al.  The nature of mathematical modeling , 1998 .

[4]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[5]  Cândida Ferreira.  Gene Expression Programming in Problem Solving , 2002 .

[6]  Hitoshi Iba,et al.  Inference of differential equation models by genetic programming , 2002, Inf. Sci..

[7]  Mihai Oltean,et al.  A Comparison of Several Linear Genetic Programming Techniques , 2003, Complex Syst..

[8]  Lishan Kang,et al.  Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming , 2000, Genetic Programming and Evolvable Machines.

[9]  Yuehui Chen,et al.  Evolving Additive Tree Models for System Identification , 2005 .

[10]  S. Hartmann,et al.  Models in Science , 2006 .

[11]  R. Naresh,et al.  A nonlinear mathematical model to study the interactions of hot gases with cloud droplets and raindrops , 2009 .

[12]  Feng Ding,et al.  Hierarchical Least Squares Identification for Linear SISO Systems With Dual-Rate Sampled-Data , 2011, IEEE Transactions on Automatic Control.

[13]  Yuehui Chen,et al.  Time-series forecasting using a system of ordinary differential equations , 2011, Inf. Sci..

[14]  Mingyan Jiang,et al.  An Improved Artificial Bee Colony Algorithm Based on Gaussian Mutation and Chaos Disturbance , 2012, ICSI.

[15]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[16]  L. Samavedham,et al.  Genetic programming-based approach to elucidate biochemical interaction networks from data. , 2013, IET systems biology.

[17]  S. R. Thomas,et al.  Hormonal regulation of salt and water excretion: a mathematical model of whole kidney function and pressure natriuresis. , 2014, American journal of physiology. Renal physiology.