Intelligent control for automotive manufacturing-rule based guided adaptation

Filev, Dimitar; Larsson, T.; Ma, Lixing

doi:10.1109/iecon.2000.973164

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…It should be noted that using the potential instead of the distance to a certain rule center only [15], [27], [31] for forming the rule-base results in rules that are more informative and a more compact rule-base. The reason is that the spatial information and history are not ignored, but are part of the decision whether to upgrade or modify the rule-base.…”

Section: A Online Clustering Approachmentioning

confidence: 99%

“…In addition, the mechanism of rule-base modification (replacement of a less informative rule with a more informative one) is considered in [9]. It is also based on the informative potential and is more conservative than the replacement used in [15], [27], [31] ensuring a gradual change of the rule-base structure and inheritance of the structural information. models generate a new rule if there is significant new information present in the data collected.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, rule-bases [9], [15], [27] and neural networks [20], [31] with evolving structure have been developed. Rule-base of Initial Conditions, RBIC [15], Intelligent Model Bank [27] and the Self-constructing fuzzy-neural network controller [20] are primarily oriented to control applications.…”

Section: Introductionmentioning

confidence: 99%

“…Rule-base of Initial Conditions, RBIC [15], Intelligent Model Bank [27] and the Self-constructing fuzzy-neural network controller [20] are primarily oriented to control applications. They use different mechanism of rules update based on the distance to certain rule center [15], [27], [31] or the error in previous steps [20].…”

Section: Introductionmentioning

confidence: 99%

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An Approach to Online Identification of Takagi-Sugeno Fuzzy Models

Angelov

Filev

2004

IEEE Trans. Syst., Man, Cybern. B

939

620

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Abstract-An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.

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Section: A Online Clustering Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Approach to Online Identification of Takagi-Sugeno Fuzzy Models

Angelov

Filev

2004

IEEE Trans. Syst., Man, Cybern. B

939

620

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show abstract

“…Algorithms for on-line applications with self-constructing or evolving structure have been reported very recently and independently for the fuzzy [8]- [17] and NN models [5], [9], [21]- [22]. In [5], however, the learning scheme is iterative and tends to fall into local minimums, because it is a gradient-based error back-propagation.…”

Section: Flexible Models With Evolving Structurementioning

confidence: 99%

Flexible models with evolving structure

Angelov

Filev

2004

Int. J. Intell. Syst.

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Abstract A type of flexible models in the form of a neural network (NN) with evolving structure is treated in the paper. We refer to models with amorphous structure as flexible models. There is a close link between different types of flexible models: fuzzy models, fuzzy NN, and general regression model. All of them are proven universal approximators and some of them (Takagi-Sugeno fuzzy model with singleton outputs and radial-basis function NN) are interchangeable. The evolving NN (eNN) considered here makes use of the recently introduced on-line approach to identification of TakagiSugeno fuzzy models with evolving structure (eTS). Both eR and eNN differ from the other model schemes by their gradually evolving structure as opposed to the fixed structure models, in which only parameters are subject to optimization or adaptation. The learning algorithm is incremental, and combines unsupervised on-line recursive clustering and supervised recursive on-line output parameter estimation. eNN has potential in modeling, control (if combined with the indirect learning mechanism), fault detection and diagnostics etc. Its computational efficiency is based on the non-iterative and recursive procedure, which combines Kalman filter with proper initializations, and on-line unsupervised clustering. eNN has been tested with data from a real air-conditioning installation. Applications to real-time adaptive non-linear control, fault detection and diagnostics, performance analysis, time-series forecasting, knowledge extraction and accumulation, etc. are possible directions of their use in the future research.Index Terms Artificial Neural Networks, Takagi-Sugeno and Evolving Rule-based Models, Subtractive Clustering I.

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Evolving Fuzzy Systems

Angelov

2009

Encyclopedia of Complexity and Systems Science

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Intelligent control for automotive manufacturing-rule based guided adaptation

Cited by 17 publications

References 4 publications

An Approach to Online Identification of Takagi-Sugeno Fuzzy Models

An Approach to Online Identification of Takagi-Sugeno Fuzzy Models

Flexible models with evolving structure

Evolving Fuzzy Systems

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