Automatic tuning of complex fuzzy systems with Xfuzzy

Moreno-Velo, F.J.; Baturone, I.; Barriga, A.; Sánchez-Solano, Santiago

doi:10.1016/j.fss.2007.03.006

Cited by 44 publications

(40 citation statements)

References 16 publications

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“…For this study, we used the implementations in the Xfuzzy environment, see [39] for a more detailed description of the wide range of methods supported. Among them, we distinguish four classes of methods: gradient descent [32], conjugate gradient, second order or quasi-Newton [3], and algorithms with no derivatives.…”

Section: B Comparison Of Different Neuro-fuzzy Methodsmentioning

confidence: 99%

“…It is worth to mention that none of the statistical or probabilistic methods was found to be competitive in terms of performance, being unable to achieve training errors below the DT based threshold in most cases, within reasonable time bounds. These include the Simulated Annealing method with different cooling schemes, Downhill Simplex and Powell´s methods [39]. We note however that these methods are highly dependent on the values of several parameters that could be explored only partially.…”

Section: B Comparison Of Different Neuro-fuzzy Methodsmentioning

confidence: 99%

“…The W&M algorithm is based on the "learn by example" principle and considers a uniform grid partition of the universes of discourse of the inputs, which are proper characteristics for modeling time series in an interpretable manner. Though a number of modifications and derived algorithms have been proposed, for the sake of simplicity and interpretability we adhere to the original specification of the algorithm for generating fuzzy inference rules directly from input-output data pairs [50] as implemented in version 3.2 of the Xfuzzy design environment [40].…”

Section: Substage 21: System Identificationmentioning

confidence: 99%

See 2 more Smart Citations

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Pouzols¹,

Lendasse²,

Barriga³

2010

Fuzzy Sets and Systems

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We propose an automatic methodology framework for short-and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications

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Section: B Comparison Of Different Neuro-fuzzy Methodsmentioning

confidence: 99%

Section: B Comparison Of Different Neuro-fuzzy Methodsmentioning

confidence: 99%

Section: Substage 21: System Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Pouzols¹,

Lendasse²,

Barriga³

2010

Fuzzy Sets and Systems

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show abstract

“…For the concrete implementation analyzed in this paper, identification is performed using the W&M algorithm driven by the DT estimate. Though many modifications to the original algorithm have been proposed throughout the years, for the sake of simplicity we adhere to the original algorithm specification in [1] as implemented in version 3.2 of the Xfuzzy design environment [8].…”

Section: B System Identification and Tuningmentioning

confidence: 99%

“…All the parameters of the membership functions of every input and output are adjusted using the algorithm implementation in the Xfuzzy development environment [10].…”

Section: ) Stage 22: System Tuningmentioning

confidence: 99%

Fuzzy inference based autoregressors for time series prediction using nonparametric residual variance estimation

Pouzols

Lendasse²,

Barriga

2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

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Abstract-We apply fuzzy techniques for system identification and supervised learning in order to develop fuzzy inference based autoregressors for time series prediction. An automatic methodology framework that combines fuzzy techniques and statistical techniques for nonparametric residual variance estimation is proposed. Identification is performed through the learn from examples method introduced by Wang and Mendel, while the Marquard-Levenberg supervised learning algorithm is then applied for tuning. Delta test residual noise estimation is used in order to select the best subset of inputs as well as the number of linguistic labels for the inputs. Experimental results for three time series prediction benchmarks are compared against LS-SVM based autoregressors and show the advantages of the proposed methodology in terms of approximation accuracy, generalization capability and linguistic interpretability.

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Direct adaptive fuzzy control with membership function tuning

Moradi

Kazemi

Ershadi

2011

Asian Journal of Control

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In this paper, a direct self-structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation.

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Automatic tuning of complex fuzzy systems with Xfuzzy

Cited by 44 publications

References 16 publications

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Fuzzy inference based autoregressors for time series prediction using nonparametric residual variance estimation

Direct adaptive fuzzy control with membership function tuning

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