Parameter identification for piecewise-affine fuzzy models in noisy environment

Simani, Silvio; Fantuzzi, Cesare; Rovatti, Riccardo; Beghelli, S.

doi:10.1016/s0888-613x(99)00012-2

Cited by 51 publications

(120 citation statements)

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“…A large part of fuzzy modelling and identification algorithms (see, e.g., Simani et al, 1999) share a common two-step procedure, in which at first the operating regions are determined using heuristics or data clusterings techniques. Then, in the second stage, the identification of the parameters of each submodel is achieved using a suitable estimation algorithm.…”

Section: Fuzzy Identification From Datamentioning

confidence: 99%

“…The consequent parameters a i and b i are estimated from the data using the method developed by the author (Simani et al, 1999) and recalled below. This identification scheme exploited for the estimation of TS model parameters has been integrated into the FMID toolbox for Matlab R by the author.…”

Section: 11mentioning

confidence: 99%

“…This approach developed by the author is usually preferred when the TS model should serve as a predictor, as it computes the consequent parameters via the Frisch scheme, developed for errors-in-variables descriptions (Van Huffel and Lemmerling, 2002). Therefore, after the clustering of the data has been obtained via the GK algorithm (Babuška, 1998), the data subsets are processed according to the Frisch scheme identification procedure (Simani et al, 1999), in order to estimate the TS parameters for each affine submodel.…”

Section: 11mentioning

confidence: 99%

See 2 more Smart Citations

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Simani¹

2013

International Journal of Applied Mathematics and Computer Science

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Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

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Section: Fuzzy Identification From Datamentioning

confidence: 99%

Section: 11mentioning

confidence: 99%

Section: 11mentioning

confidence: 99%

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Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Simani¹

2013

International Journal of Applied Mathematics and Computer Science

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“…The switched linear system was converted to the well-known mixed logical dynamical framework and model parameters were obtained using mixed integer program. On the other hand, Billings and Voon [15] and Simani et al [16] partitioned the state-space independent of the identification data. Billings and Voon [15] used a rectangular partitioning parallel to coordinate axes while Simani et al [16] used a simplicial partition.…”

Section: A Brief Review On Hybrid System Identificationmentioning

confidence: 99%

“…On the other hand, Billings and Voon [15] and Simani et al [16] partitioned the state-space independent of the identification data. Billings and Voon [15] used a rectangular partitioning parallel to coordinate axes while Simani et al [16] used a simplicial partition. A Bayesian approach for identifying model parameters that considers model parameters as random variables has been proposed by Juloski et al [17].…”

Section: A Brief Review On Hybrid System Identificationmentioning

confidence: 99%