A Matlab/Simulink toolbox for inversion of local linear model trees

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

A Matlab/Simulink toolbox for inversion of local linear model trees. / Nentwig, M.; Mercorelli, P.
In: IAENG International Journal of Computer Science, Vol. 37, No. 1, 02.2010, p. 19-26.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

APA

Vancouver

Bibtex

@article{1342729c88b94e43ba6e1a9ef5935162,
title = "A Matlab/Simulink toolbox for inversion of local linear model trees",
abstract = "Models in the form of characteristic diagrams or more specifically, in the form of engine operating maps are mostly used in the automobile industry. This yields a large amount of measurements and involves the use of advanced instrumentations. This paper shows a developed software environment, namely a toolbox for the program Matlab/Simulink developed by company Mathworks. The name of the toolbox is {"}Inversion of the Local Linear Model Trees{"} and it basically consists of a local inversion of the Local Linear Model Trees (LOLIMOT). The importance of the inversion in control problems is widely known. Neural networks are a very effective and popular tools mostly used for modeling. The inversion of a neural network produces real possibilities to involve the networks in the control problem schemes. The developed toolbox is explained with the help of diagrams and GUI structure from Matlab which tend to clarify the idea of the program and its structure. The presentation is organized as a short tutorial of the toolbox, so that a potential user can directly understand how to access it. Nevertheless, formal mathematical equations, concerning the neural networks and membership functions, need to be explained together with the LOLIMOT structure. To validate and to clarify the explained toolbox, an example from a system used in the automobile industry is briefly shown.",
keywords = "Engineering, Inversion, Local linear model trees (LOLIMOT), Matlab/Simulink, Neuro-fuzzy identification, Nonlinear systems",
author = "M. Nentwig and P. Mercorelli",
note = "Export Date: 22 May 2012 Source: Scopus Language of Original Document: English Correspondence Address: Nentwig, M.; University of Applied Sciences Ostfalia, Department of Automotive Engineering, Robert-Koch-Platz 10 - 14, 38440 Wolfsburg, Germany; email: mail@mnentwig.de References: Toepfer, A., Fink, A., (2003), Technical report - on the inversion of nonlinear modelsIsermann, R., Fink, A., Toepfer, S., Neuro and neurofuzzy identification for model-based control (2001) IFAC Workshop on Advanced Fuzzy/Neural Control, Valencia, Spain, Q, pp. 111-116; Isermann, R., Fink, A., Toepfer, S., Nonlinear modelbased control with local linear neuro-fuzzy models (2003) Archive of Applied Mechanics, 72 (11-12), pp. 911-922; Fink, A., Nelles, O., Nonlinear internal model control based on local linear neural networks (2001) IEEE Systems, Man, and Cybernetics, Tucson, , USA; Mercorelli, P., An optimal minimum phase approximating pd regulator for robust control of a throttle plate (2006) 45th IEEE Conference on Decision and Control (CDC2006), , San Diego (USA), 13th-15th December; Nelles, O., (1999) Nonlinear System Identification with Local Linear Neuro-Fuzzy Models, , Shaker Verlag; Isermann, R., Nelles, O., Fink, A., Local linear model trees (lolimot) toolbox for nonlinear system identification (2000) 12th IFAC Symposium on System Identification (SYSID), , Santa Barbara, USA",
year = "2010",
month = feb,
language = "English",
volume = "37",
pages = "19--26",
journal = "IAENG International Journal of Computer Science",
issn = "1819-656X",
publisher = "International Association of Engineers",
number = "1",

}

RIS

TY - JOUR

T1 - A Matlab/Simulink toolbox for inversion of local linear model trees

AU - Nentwig, M.

AU - Mercorelli, P.

N1 - Export Date: 22 May 2012 Source: Scopus Language of Original Document: English Correspondence Address: Nentwig, M.; University of Applied Sciences Ostfalia, Department of Automotive Engineering, Robert-Koch-Platz 10 - 14, 38440 Wolfsburg, Germany; email: mail@mnentwig.de References: Toepfer, A., Fink, A., (2003), Technical report - on the inversion of nonlinear modelsIsermann, R., Fink, A., Toepfer, S., Neuro and neurofuzzy identification for model-based control (2001) IFAC Workshop on Advanced Fuzzy/Neural Control, Valencia, Spain, Q, pp. 111-116; Isermann, R., Fink, A., Toepfer, S., Nonlinear modelbased control with local linear neuro-fuzzy models (2003) Archive of Applied Mechanics, 72 (11-12), pp. 911-922; Fink, A., Nelles, O., Nonlinear internal model control based on local linear neural networks (2001) IEEE Systems, Man, and Cybernetics, Tucson, , USA; Mercorelli, P., An optimal minimum phase approximating pd regulator for robust control of a throttle plate (2006) 45th IEEE Conference on Decision and Control (CDC2006), , San Diego (USA), 13th-15th December; Nelles, O., (1999) Nonlinear System Identification with Local Linear Neuro-Fuzzy Models, , Shaker Verlag; Isermann, R., Nelles, O., Fink, A., Local linear model trees (lolimot) toolbox for nonlinear system identification (2000) 12th IFAC Symposium on System Identification (SYSID), , Santa Barbara, USA

PY - 2010/2

Y1 - 2010/2

N2 - Models in the form of characteristic diagrams or more specifically, in the form of engine operating maps are mostly used in the automobile industry. This yields a large amount of measurements and involves the use of advanced instrumentations. This paper shows a developed software environment, namely a toolbox for the program Matlab/Simulink developed by company Mathworks. The name of the toolbox is "Inversion of the Local Linear Model Trees" and it basically consists of a local inversion of the Local Linear Model Trees (LOLIMOT). The importance of the inversion in control problems is widely known. Neural networks are a very effective and popular tools mostly used for modeling. The inversion of a neural network produces real possibilities to involve the networks in the control problem schemes. The developed toolbox is explained with the help of diagrams and GUI structure from Matlab which tend to clarify the idea of the program and its structure. The presentation is organized as a short tutorial of the toolbox, so that a potential user can directly understand how to access it. Nevertheless, formal mathematical equations, concerning the neural networks and membership functions, need to be explained together with the LOLIMOT structure. To validate and to clarify the explained toolbox, an example from a system used in the automobile industry is briefly shown.

AB - Models in the form of characteristic diagrams or more specifically, in the form of engine operating maps are mostly used in the automobile industry. This yields a large amount of measurements and involves the use of advanced instrumentations. This paper shows a developed software environment, namely a toolbox for the program Matlab/Simulink developed by company Mathworks. The name of the toolbox is "Inversion of the Local Linear Model Trees" and it basically consists of a local inversion of the Local Linear Model Trees (LOLIMOT). The importance of the inversion in control problems is widely known. Neural networks are a very effective and popular tools mostly used for modeling. The inversion of a neural network produces real possibilities to involve the networks in the control problem schemes. The developed toolbox is explained with the help of diagrams and GUI structure from Matlab which tend to clarify the idea of the program and its structure. The presentation is organized as a short tutorial of the toolbox, so that a potential user can directly understand how to access it. Nevertheless, formal mathematical equations, concerning the neural networks and membership functions, need to be explained together with the LOLIMOT structure. To validate and to clarify the explained toolbox, an example from a system used in the automobile industry is briefly shown.

KW - Engineering

KW - Inversion

KW - Local linear model trees (LOLIMOT)

KW - Matlab/Simulink

KW - Neuro-fuzzy identification

KW - Nonlinear systems

UR - http://www.scopus.com/inward/record.url?scp=77956486937&partnerID=8YFLogxK

M3 - Journal articles

VL - 37

SP - 19

EP - 26

JO - IAENG International Journal of Computer Science

JF - IAENG International Journal of Computer Science

SN - 1819-656X

IS - 1

ER -

Recently viewed

Publications

  1. Using Language Learning Resources on YouTube
  2. Cognitive Predictors of Child Second Language Comprehension and Syntactic Learning
  3. A Theoretical Dynamical Noninteracting Model for General Manipulation Systems Using Axiomatic Geometric Structures
  4. Teachers’ use of data from digital learning platforms for instructional design
  5. Dynamic environment modelling and prediction for autonomous systems
  6. Machine Learning and Knowledge Discovery in Databases
  7. Multiphase-field modeling of temperature-driven intermetallic compound evolution in an Al-Mg system for application to solid-state joining processes
  8. Guided discovery learning with computer-based simulation games
  9. Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids
  10. A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods
  11. Quantifying diffuse and point inputs of perfluoroalkyl acids in a nonindustrial river catchment
  12. Is too much help an obstacle? Effects of interactivity and cognitive style on learning with dynamic versus non-dynamic visualizations with narrative explanations
  13. An application of multiple behavior SIA for analyzing data from student exams
  14. How, when and why do negotiators use reference points?
  15. Watershed groundwater balance estimation using streamflow recession analysis and baseflow separation
  16. Learning and Re-learning from net- based cooperative learning discourses
  17. Developing a Complex Portrait of Content Teaching for Multilingual Learners via Nonlinear Theoretical Understandings
  18. A PD regulator to minimize noise effect using a minimal variance method for soft landing control of an electromagnetic valve actuator
  19. Soil conditions modify species diversity effects on tree functional trait expression
  20. Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics
  21. Solving mathematical problems with dynamical sketches
  22. Toward a methodical framework for comprehensively assessing forest multifunctionality
  23. Bayesian Parameter Estimation in Green Business Process Management
  24. Performance incentives in activity-based management
  25. Experiments on the Fehrer-Raab effect and the ‘Weather Station Model’ of visual backward masking
  26. Distributed robust Gaussian Process regression
  27. Understanding Partnering Strategies in the Low-Code Platform Ecosystem
  28. A MODEL FOR QUANTIFICATION OF SOFTWARE COMPLEXITY
  29. Influence of Process Parameters and Die Design on the Microstructure and Texture Development of Direct Extruded Magnesium Flat Products
  30. Introduction Mobile Digital Practices. Situating People, Things, and Data
  31. Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning
  32. Learning from Erroneous Examples