Skip to main content
×
Home
    • Aa
    • Aa

A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems

  • Ahmed A. Adeniran (a1) and Sami El Ferik (a1)
Abstract
Abstract

Several industrial systems are characterized by high nonlinearities with wide operating ranges and large set point changes. Identification and representation of these systems represent a challenge, especially for control engineers. Multimodel technique is one effective approach that can be used to describe nonlinear systems through the combination of several submodels, where each is contributing to the output with a certain degree of validity. One major concern in this technique, especially for systems with unknown operating conditions, is the partitioning of the system's operating space and thus the identification of different submodels. This paper proposes a three-stage approach to obtain a multimodel representation of a nonlinear system. A reinforced combinatorial particle swarm optimization and hybrid K-means are used to determine the number of submodels and their respective parameters. The proposed method automatically optimizes the number of submodels with respect to the submodel complexity. This allows operating space partition and generation of a parsimonious number of submodels without prior knowledge. The application of this approach on several examples, including a continuous stirred tank reactor, demonstrates its effectiveness.

Copyright
Corresponding author
Reprint requests to: Ahmed A. Adeniran, Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia. E-mail: selferik@kfupm.edu.sa
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

F.-C. Chen , & H. Khalil (1995). Adaptive control of a class of nonlinear discrete-time systems using neural networks. IEEE Transactions on Automatic Control 40(5), 791801.

K. Demirli , S.X. Chengs , & P. Muthukumaran (2003). Subtractive clustering based on modeling of job sequencing with parametric search. Fuzzy Sets and Systems 137(2), 235270.

R. DeVeaux , D. Psichogios , & L.H. Ungar (1993). A comparison of two nonparametric estimation schemes: Mars and neural networks. Computers in Chemical Engineering 17(8), 819837.

J. Du , C. Song , & P. Li (2009). Application of gap metric to model bank determination in multilinear model approach. Journal of Process Control 19(2), 231240.

N. Elfelly , J. Dieulot , M. Benrejeb , & P. Borne (2010). A new approach for multi-model identification of complex systems based on both neural and fuzzy clustering algorithms. Engineering Applications of Artificial Intelligence 23(7), 10641071.

G. Gregorčič , & G. Lightbody (2007). Local model network identification with Gaussian processes. IEEE Transactions on Neural Networks 18(9), 14041423.

Y.-J. Hu , Y. Wang , Z.-L. Wang , Y.-Q. Wang , & B.C. Zhang (2014). Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(2), 7182.

B. Jarboui , M. Cheikh , P. Siarry , & A. Rebai (2007). Combinatorial particle swarm optimization (cpso) for partitional clustering problem. Applied Mathematics and Computation 192(2), 337345.

B. Majhi , & G. Panda (2011). Robust identification of nonlinear complex systems using low complexity {ANN} and particle swarm optimization technique. Expert Systems With Applications 38(1), 321333.

H. Modares , A. Alfi , & M.-M. Fateh (2010). Parameter identification of chaotic dynamic systems through an improved particle swarm optimization. Expert Systems With Applications 37(5), 37143720.

R.B. Mohamed , H. Ben Nasr , & F.M. Sahli (2011). A multi-model approach for a nonlinear system based on neural network validity. International Journal of Intelligent Computing and Cybernetics 4(3), 331352.

K. Narendra , & K. Parthasarathy (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(1), 427.

N.L. Nihan , & G.A. Davis (1987). Recursive estimation of origin-destination matrices from input/output counts. Transportation Research Part B: Methodological 21(2), 149163.

R. Orjuela , B. Marx , J. Ragot , & D. Maquin (2013). Nonlinear system identification using heterogeneous multiple models. International Journal of Applied Mathematics and Computer Science 23(1), 103115.

W. Pan , Y. Yuan , J. Goncalves , & G. Stan (2016). A sparse Bayesian approach to the identification of nonlinear state-space systems. IEEE Transactions on Automatic Control 61(1), 182187.

M. Ronen , Y. Shabtai , & H. Guterman (2002). Hybrid modeling building methodology using unsupervised fuzzy clustering and supervised neural networks. Biotechnology and Bioengineering 77(4), 420429.

M. Scarpiniti , D. Comminiello , R. Parisi , & A. Uncini (2015). Novel cascade spline architectures for the identification of nonlinear systems. IEEE Transaction on Circuits and Systems I: Regular Papers 62(7), 18251835.

D. Simon (2010). Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. Control Theory Applications IET 4(8), 13031318.

D. Simon , & T.L. Chia (2002). Kalman filtering with state equality constraints. IEEE Transactions on Aerospace and Electronic Systems 38(1), 128136.

R. Stanforth , E. Kolossov , & B. Mirkin (2007). Hybrid k-means: combining regressionwise and centroid-based criteria for qsar. In Selected Contributions in Data Analysis and Classification: Studies in Classification, Data Analysis, and Knowledge Organization ( P. Brito , G. Cucumel , P. Bertrand , & F. Carvalho , Eds.), pp. 225233. Berlin: Springer.

Y. Tang , L. Qiao , & X. Guan (2010). Identification of wiener model using step signals and particle swarm optimization. Expert Systems With Applications 37(4), 33983404.

L. Teslic , B. Hartmann , O. Nelles , & I. Škrjanc (2011). Nonlinear system identification by Gustafson-Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process. IEEE Transactions on Neural Networks 22(12), 19411951.

A.N. Venkat , P. Vijaysai , & R.D. Gudi (2003). Identification of complex nonlinear process based on fuzzy decomposition of the steady state space. Journal of Process Control 13(6), 473488.

V. Verdult , L. Ljung , & M. Verhaegen (2002). Identification of composite local linear statespace models using a projected gradient search. International Journal of Control 75(16), 13851398.

C. Wen , S. Wang , X. Jin , & X. Ma (2007). Identification of dynamic systems using piecewise-affine basis function models. Automatica 43(10), 18241831.

E. Wernholt , & S. Moberg (2011). Nonlinear gray-box identification using local models applied to industrial robots. Automatica 47(4), 650660.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 3
Total number of PDF views: 38 *
Loading metrics...

Abstract views

Total abstract views: 235 *
Loading metrics...

* Views captured on Cambridge Core between 5th December 2016 - 25th September 2017. This data will be updated every 24 hours.