Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014

Dieser Tagungsband enthält die Beiträge des 24. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 27. - 28. November 2014 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015

Dieser Tagungsband enthält die Beiträge des 25. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) , der vom 26. – 27. November 2015 in Dortmund stattfindet

Proceedings. 22. Workshop Computational Intelligence, Dortmund, 6. - 7. Dezember 2012

Dieser Tagungsband enthält die Beiträge des 22. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) der vom 6. - 7. Dezember 2012 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für - Fuzzy-Systeme, - Künstliche Neuronale Netze, - Evolutionäre Algorithmen und - Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018

Dieser Tagungsband enthält die Beiträge des 28. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen

Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5. - 6. Dezember 2013

Dieser Tagungsband enthält die Beiträge des 23. Workshops Computational Intelligence des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 5. - 6. Dezember 2013 in Dortmund stattgefunden hat. Im Fokus stehen Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren

Reduced order modelling through system identification using stochastic filtering

This thesis presents a novel approach to model order reduction, through system identification and using stochastic filtering. Order reduction is a particularly relevant application in the automotive context, as the generation of simplified simulation models for the whole vehicle and its subsystems is an increasingly important aspect of vehicle design. First, grey-box parameter identification of vehicle handling dynamics is explored, including identification of a combined-slip tyre model. This introductory study serves as an intermediate step to review three alternative stochastic filters: identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are here compared for effectiveness, complexity and computational efficiency. Despite being initially merely considered as a stepping stone towards black-box identification, this phase of the PhD generated its own and independent outcomes and might be viewed as a spin-off of the main research topic. All three filters appear suited to system identification and could operate in on-line model predictive controllers or estimators, with varying levels of practicability at different sampling rates. Work on black-box system identification then starts through a non-linear Kalman filter, extended to identify all the parameters of a canonical linear state-space structure. In spite of all model parameters being unknown at the start, the filter is able to evolve parameter estimates to achieve 100$\%$ accuracy in noise-free test cases, and is also proven to be robust to noise in the measurements. The canonical form ensures that a minimal number of parameters need to be identified and produces additional information in terms of eigenvalues and dominant modes. After extensive testing in the linear domain, state-space is extended into a non-linear framework, with each parameter becoming a non-linear function of system inputs or states. Parameter variation is first constrained by cubic spline polynomials, to provide continuity and maintain relatively small extended state-parameter vectors. This early approach is later simplified, with each element of state-space generated through unconstrained, generic non-linear functions and defined through a number of equally spaced, fixed nodes. Conditioning and convergence are maintained through the definition of additional system outputs, based on specific functions of the non-linear node ordinates. Unlike other methods published in the literature, this new approach does not focus on a specific non-linear structure, but consists in the prescription of a generic and yet simple non-linear state-space model structure, that allows various non-linearities to be identified and approximated solely based on inputs and outputs. The method is illustrated in practice through simple non-linear examples and test cases, which include the identification of a full vehicle model, a highly non-linear brake model and CFD data. These applications show that it is possible to easily expand the order of the system and the complexity of the non-linearities, to achieve higher accuracy while ensuring good parameter conditioning. The approach is completely black-box and requires no physical understanding of the process for successful identification, making it an ideally suited mechanism for order reduction of high order simulation models. In addition to high order simulation data, the developed approach can be used as a tool for conventional system identification and applied to experimental test data as well.</div

Proc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023

Dieser Tagungsband enthält die Beiträge des 33. Workshops „Computational Intelligence“ der vom 23.11. – 24.11.2023 in Berlin stattfindet. Die Schwerpunkte sind Methoden, Anwendungen und Tools für ° Fuzzy-Systeme, ° Künstliche Neuronale Netze, ° Evolutionäre Algorithmen und ° Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen.The workshop proceedings contain the contributions of the 33rd workshop "Computational Intelligence" which will take place from 23.11. - 24.11.2023 in Berlin. The focus is on methods, applications and tools for ° Fuzzy systems, ° Artificial Neural Networks, ° Evolutionary algorithms and ° Data mining methods as well as the comparison of methods on the basis of industrial and benchmark problems

Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzy modelling becomes an attractive and widely used paradigm for this purpose. However, fuzzy models constructed from data by adaptive learning algorithms usually suffer from the loss of model interpretability. Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community. In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of low-level interpretability and high-level interpretability in this paper. The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level. Some criteria for low-level interpretability and high-level interpretability are identified, respectively. Different data-driven fuzzy modelling techniques in the literature focusing on the interpretability issues are reviewed and discussed from the perspective of low-level interpretability and high-level interpretability. Furthermore, some open problems about interpretable fuzzy models are identified and some potential new research directions on fuzzy model interpretability are also suggested. Crown Copyright © 2008