Document Type : Research Articles

Authors

1 Control Engineering Dept., Electrical Faculty K.N.Toosi University of Technology, Tehran, Iran

2 Mechatronics Dept., Electrical Faculty K.N.Toosi University of Technology, Tehran, Iran

Abstract

The Local Model Network (LMN) is one of the common structures to model systems and fault detection and identification. This structure covers the disadvantages of training in fuzzy systems and interpretations in neural networks at the same time. But the algorithms that have been introduced to create LMN, such as LOLIMOT, are very sensitive to the dimension of input space. In other words, the search space and the number of network parameters are increased exponentially by increasing the input dimension, which is called the curse of dimensionality. Therefore in this paper, the LMN structure has been developed, and a new incremental algorithm has been proposed which is based on Genetic algorithm and LOLIMOT algorithm that is called GLOLIMOT. The proposed idea reduces the search space dimension and also optimizes it. The proposed idea and the traditional structure are tested on single-shaft industrial gas turbine prototype model, which has high complexity and high dimension. The results indicate improvement in performance of the proposed structure and algorithm.

Keywords

Main Subjects

[1] T. Fischer, B. Hartmann, and O. Nelles, "Increasing the
Performance of a Training Algorithm for Local Model
Networks," in World Congress of Engineering and
Computer Science (WCECS). San Francisco, USA, 2012.
[2] A. A. Adeniran and S. El Ferik, "Modeling and
Identification of Nonlinear Systems: A Review of the
Multimodel Approach--Part 1," 2016.
[3] O. Nelles, S. Sinsel, and R. Isermann, "Local basis
function networks for identification of a turbocharger," in
Control'96, UKACC International Conference on (Conf.
Publ. No. 427), 1996, pp. 7-12.
[4] O. Nelles, Nonlinear system identification: from classical
approaches to neural networks and fuzzy models: Springer
Science & Business Media, 2013.
[5] T. A. Johansen and B. A. Foss, "Identification of
non-linear system structure and parameters using regime
decomposition," Automatica, vol. 31, pp. 321-326, 1995.
[6] O. Bänfer and O. Nelles, "Polynomial model tree
(POLYMOT)—A new training algorithm for local model
networks with higher degree polynomials," in 2009 IEEE
International Conference on Control and Automation,
2009, pp. 1571-1576.
[7] M. A. Nekoui and S. M. Sajadifar, "Nonlinear System
Identification using Locally Linear Model Tree and
Particle Swarm Optimization," in Industrial Technology,
2006. ICIT 2006. IEEE International Conference on, 2006,
pp. 1563-1568.
[8] R. Mehran, A. Fatehi, C. Lucas, and B. N. Araabi, "Particle
swarm extension to LOLIMOT," in Sixth International
Conference on Intelligent Systems Design and
Applications, 2006, pp. 969-974.
[9] J. Rezaie, B. Moshiri, A. Rafati, and B. N. Araabi,
"Modified LOLIMOT algorithm for nonlinear centralized
Kalman filtering fusion," in Information Fusion, 2007 10th
International Conference on, 2007, pp. 1-8.
[10] S. Jakubek and C. Hametner, "Identification of neurofuzzy
models using GTLS parameter estimation," IEEE
Transactions on Systems, Man, and Cybernetics, Part B
(Cybernetics), vol. 39, pp. 1121-1133, 2009.
[11] S. Jakubek and N. Keuth, "A local neuro-fuzzy network for
high-dimensional models and optimization," Engineering
applications of artificial intelligence, vol. 19, pp. 705-717,
2006.
[12] A. Sarabi-Jamab and B. N. Araabi, "PiLiMoT: A Modified
Combination of LoLiMoT and PLN Learning Algorithms
for Local Linear Neurofuzzy Modeling," Journal of
Control Science and Engineering, vol. 2011, 2011.
[13] A. S. Jamab and B. N. Araabi, "A learning algorithm for
local linear neuro-fuzzy models with self-construction
through merge & split," in 2006 IEEE Conference on
Cybernetics and Intelligent Systems, 2006, pp. 1-6.
[14] S. M. E. Oliaee, M. A. Shoorehdeli, and M. Teshnehlab,
"Faults detecting of high-dimension gas turbine by
stacking DNN and LLM," in Fuzzy and Intelligent Systems
(CFIS), 2018 6th Iranian Joint Congress on, 2018, pp.
142-145.
[15] D. Karaboga and E. Kaya, "Adaptive network based fuzzy
inference system (ANFIS) training approaches: a
comprehensive survey," Artificial Intelligence Review, pp.
1-31, 2018.
[16] A. Doroshenko, "Piecewise-Linear Approach to
Classification Based on Geometrical Transformation
Model for Imbalanced Dataset," in 2018 IEEE Second
International Conference on Data Stream Mining &
Processing (DSMP), 2018, pp. 231-235.
[17] H. Azimi, S. Shabanlou, I. Ebtehaj, H. Bonakdari, and S.
Kardar, "Combination of computational fluid dynamics,
adaptive neuro-fuzzy inference system, and genetic
algorithm for predicting discharge coefficient of
rectangular side orifices," Journal of Irrigation and
Drainage Engineering, vol. 143, p. 04017015, 2017.
[18] A. H. Hamamoto, L. F. Carvalho, L. D. H. Sampaio, T.
Abrão, and M. L. Proença Jr, "Network anomaly detection
system using genetic algorithm and fuzzy logic," Expert
Systems with Applications, vol. 92, pp. 390-402, 2018.
[19] O. F. Lutfy, S. B. M. Noor, and M. H. Marhaban, "A
simplified adaptive neuro-fuzzy inference system (ANFIS)
controller trained by genetic algorithm to control nonlinear
multi-input multi-output systems," Scientific Research and
Essays, vol. 6, pp. 6475-6486, 2011.
[20] L. Breiman, "Hinging hyperplanes for regression,
classification, and function approximation," IEEE
Transactions on Information Theory, vol. 39, pp.
999-1013, 1993.
[21] S. Ernst, "Hinging hyperplane trees for approximation and
identification," in Decision and Control, 1998.
Proceedings of the 37th IEEE Conference on, 1998, pp.
1266-1271.
[22] B. Hartmann, T. Ebert, T. Fischer, J. Belz, G. Kampmann,
and O. Nelles, "LMNTOOL–Toolbox zum automatischen
Trainieren lokaler Modellnetze," in Proceedings of the 22.
Workshop Computational Intelligence (Hoffmann, F.;
Hüllermeier, E., Hg.), S, 2014, pp. 341-355.
[23] O. Nelles, "Axes-oblique partitioning strategies for local
model networks," in 2006 IEEE Conference on Computer
Aided Control System Design, 2006 IEEE International
Conference on Control Applications, 2006 IEEE
International Symposium on Intelligent Control, 2006, pp.
2378-2383.
[24] B. Hartmann and O. Nelles, "On the smoothness in local
model networks," in American Control Conference (ACC),
St. Louis, USA (June 2009), 2009.
[25] B. Hartmann and O. Nelles, "Structure trade-off strategy
for local model networks," in Control Applications (CCA),
2012 IEEE International Conference on, 2012, pp.
451-456.
[26] J. Xu, X. Huang, and S. Wang, "Adaptive hinging
hyperplanes and its applications in dynamic system
identification," Automatica, vol. 45, pp. 2325-2332, 2009.
[27] S. Simani and C. Fantuzzi, "Dynamic system identification
and model-based fault diagnosis of an industrial gas
turbine prototype," Mechatronics, vol. 16, pp. 341-363,
2006.
[28] S. Simani, C. Fantuzzi, and R. Spina, "Application of a
neural network in gas turbine control sensor fault
detection," in Control Applications, 1998. Proceedings of
the 1998 IEEE International Conference on, 1998, pp.
182-186.
[29] V. Palade, R. J. Patton, F. J. Uppal, J. Quevedo, and S.
Daley, "Fault diagnosis of an industrial gas turbine using
neuro-fuzzy methods," IFAC Proceedings Volumes, vol.
35, pp. 471-476, 2002.
[30] S. Simani, "Identification and fault diagnosis of a
simulated model of an industrial gas turbine," IEEE
Transactions on Industrial Informatics, vol. 1, pp.
202-216, 2005.
[31] H. A. Nozari, M. A. Shoorehdeli, S. Simani, and H. D.
Banadaki, "Model-based robust fault detection and
isolation of an industrial gas turbine prototype using soft
computing techniques," Neurocomputing, vol. 91, pp.
29-47, 2012.
[32] O. Nelles, Nonlinear system identification: from classical
approaches to neural networks and fuzzy models: Springer,
2001.
[33] T. A. Johansen and R. Murray-Smith, "The operating
regime approach to nonlinear modelling and control,"
Multiple model approaches to modelling and control, vol.
1, pp. 3-72, 1997.
[34] V. Kecman and B. Pfeiffer, "Exploiting the structural
equivalence of learning fuzzy systems and radial basis
function neural networks," in Proceedings of the Second
European Congress on Intelligent Techniques and Soft
Computing EUFIT-94, Aachen, Gemany, 1994, pp. 58-66.
[35] S. K. Halgamuge, Advanced methods for fusion of fuzzy
systems and neural networks in intelligent data processing:
VDI Verlag, 1996.
[36] O. Nelles and B. Hartmann, "Structure Trade-off Strategy
for Local Model Networks," in IEEE International
Conference on Control Applications (CCA), Dubrovnik,
Croatia, 2012, pp. 451-456.
[37] S. Simani and R. J. Patton, "Fault diagnosis of an industrial
gas turbine prototype using a system identification
approach," Control Engineering Practice, vol. 16, pp.
769-786, 2008.
[38] S. Simani, C. Fantuzzi, and R. J. Patton, "Model-Based
Fault Diagnosis Techniques," in Model-based Fault
Diagnosis in Dynamic Systems Using Identification
Techniques, ed: Springer, 2003, pp. 19-60.