A new approach to fuzzy modeling

Kim, Euntai; Park, Minkee; Ji, Seunghwan; Park, Mignon

doi:10.1109/91.618271

Cited by 373 publications

(26 citation statements)

References 19 publications

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“…Takagi and Sugeno (TS) fuzzy model approximates nonlinear system with a combination of several linear systems by fuzzy decomposition of the entire input space into several sub-spaces and representing each input/output space with a linear equation (Kim et al 1997). …”

Section: Generating Fuzzy Models From Numerical Datamentioning

confidence: 99%

“…At this paper a method is presented to modify the initial fuzzy model instead of its reconstruction. Kim et al (1997) introduced an approach to fuzzy modeling which composed of two steps: coarse tuning and fine tuning. For coarse tuning, FCRM clustering algorithm was proposed as fuzzy rules in the form of Eq.…”

Section: Model Modification With New Datamentioning

confidence: 99%

“…In this study roughly tuned model has been developed utilizing ANFIS approach. Borrowing the basic idea for model modification from Kim et al (1997), precise model is constructed using new data by means of gradient descent algorithm method. In other words the rough model is the one that has been developed with old data where some knowledge of the system appears in the form of IF-THEN rules.…”

Section: Model Modification With New Datamentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting

Akbari

Afshar

Sadrabadi

2009

Water Resour Manage

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Reconstruction and/or modification of an already existing fuzzy model with new data may improve system performances. As new data become available, adjusting the existing fuzzy rule-based model may present a challenging alternative to full model reconstruction. In this paper a fuzzy rule-based control model using a Takagi-Sugeno fuzzy system is presented and a model modification algorithm is developed which improves the performance of the initial model as new data become available. Proposed approach is applied to a flood flow forecasting case example and the results are compared with those forecasted using initially available and reconstructed models. Results show that the modified model outperforms the initial FRB model. Reconstructed model performs slightly better than the modified model; however, the reconstruction may not be justified in a real time flood forecasting system, considering the limitations on the available lead time.

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Section: Generating Fuzzy Models From Numerical Datamentioning

confidence: 99%

Section: Model Modification With New Datamentioning

confidence: 99%

Section: Model Modification With New Datamentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting

Akbari

Afshar

Sadrabadi

2009

Water Resour Manage

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“…In order to account for the linear function property, another approach is proposed in [28]. In the approach, fuzzy subspaces and the functions in consequent parts are simultaneously identified through the use of the fuzzy C-regression model (FCRM) clustering algorithm.…”

Section: Structure Learning For Nfsmentioning

confidence: 99%

“…When outliers exist, the networks may try to fit those improper data and thus, the obtained systems may have the phenomenon of overfitting [32][33][34]. The above structure learning algorithms are all based on the principle of least square error minimization and are easily affected by outliers, which should be degraded in the clustering process [28,30,[35][36][37]. In the fine-tuning process, classical supervised learning algorithms such as gradient descent approaches are used.…”

Section: Structure Learning For Nfsmentioning

confidence: 99%

On Neural Fuzzy Systems

Yeh²

2014

International Journal of Fuzzy Logic and Intelligent Systems

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Neural fuzzy system (NFS) is basically a fuzzy system that has been equipped with learning capability adapted from the learning idea used in neural networks. Due to their outstanding system modeling capability, NFS have been widely employed in various applications. In this article, we intend to discuss several ideas regarding the learning of NFS for modeling systems. The first issue discussed here is about structure learning techniques. Various ideas used in the literature are introduced and discussed. The second issue is about the use of recurrent networks in NFS to model dynamic systems. The discussion about the performance of such systems will be given. It can be found that such a delay feedback can only bring one order to the system not all possible order as claimed in the literature. Finally, the mechanisms and relative learning performance of with the use of the recursive least squares (RLS) algorithm are reported and discussed. The analyses will be on the effects of interactions among rules. Two kinds of systems are considered. They are the strict rules and generalized rules and have difference variances for membership functions. With those observations in our study, several suggestions regarding the use of the RLS algorithm in NFS are presented.

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Extended fuzzy function model with stable learning methods for online system identification

Beyhan

Alcı

2010

Adaptive Control & Signal

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The aim of the online nonlinear system identification is the accurate modeling of the current local inputoutput behavior of the plant without using any prior knowledge and offline modeling phase. It is a challenging task for many intelligent systems when used for real-time control applications. In this paper, we propose a novel computationally efficient extended fuzzy functions (EFF) model for system identification of unknown nonlinear discrete-time systems. The main contributions are to introduce an effective quasinonlinear model (EFF) and propose adaptive learning rates (ALR) for recursive least squares (RLS) and gradient-descent (GD) methods. The asymptotic convergence of the modeling errors and boundedness of the parameters are proved by using the input-to-state stability (ISS) approach. Numerical simulations are performed for Box-Jenkins gas furnace system and a nonlinear dynamic system. The benefits of its accuracy, stability and simple implementation in practice indicate that EFF model is a promising technique for online identification of nonlinear systems.

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A new approach to fuzzy modeling

Cited by 373 publications

References 19 publications

Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting

Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting

On Neural Fuzzy Systems

Extended fuzzy function model with stable learning methods for online system identification

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