Stabilization of Rössler chaotic dynamical system using fuzzy logic control algorithm

Precup, Radu‐Emil; Tomescu, Marius-Lucian; Dragoş, Claudia-Adina

doi:10.1080/03081079.2014.893299

Cited by 83 publications

(29 citation statements)

References 48 publications

(31 reference statements)

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“…Such a robust control term can be conceived using a sliding mode control [27][28][29]33], an H?-based robust control [26,30,31] and a quasi-sliding mode control [32]. However, it is should be mentioned that the above results [26][27][28][29][30][31][32][33][34][35][36] are only applicable to chaotic systems with integer order.…”

Section: Introductionmentioning

confidence: 90%

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Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

Bouzeriba

Boulkroune

Bouden

2015

Neural Comput & Applic

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In this paper, the projective synchronization problem of two fractional-order different chaotic (or hyperchaotic) systems with both uncertain dynamics and external disturbances is considered. More particularly, a fuzzy adaptive control system is investigated for achieving an appropriate projective synchronization of unknown fractional-order chaotic systems. The adaptive fuzzy logic systems are used to approximate some uncertain nonlinear functions appearing in the system model. These latter are augmented by a robust control term to compensate for the unavoidable fuzzy approximation errors and external disturbances as well as residual error due to the use of the socalled e-modification in the adaptive laws. A Lyapunov approach is adopted for the design of the parameter adaptation laws and the proof of the corresponding stability as well as the asymptotic convergence of the underlying synchronization errors towards zero. The effectiveness of the proposed synchronization system is illustrated through numerical experiment results.

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Section: Introductionmentioning

confidence: 90%

“…Based on the universal approximation capability of the fuzzy systems [25], numerous adaptive fuzzy control schemes [26][27][28][29][30][31][32][33][34][35][36] have been developed for a class of uncertain chaotic systems but with integer order. In these schemes, the adaptive fuzzy systems are used to estimate the model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

Bouzeriba

Boulkroune

Bouden

2015

Neural Comput & Applic

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“…Fuzzy set theory [23,24] introduces the idea that a value can be partially in the set and partially outside the set, simultaneously. This is accomplished by defining a membership function that describes the degree of membership [25,26] in the set for each value.…”

Section: Fuzzy Logic-membership Functionmentioning

confidence: 99%

Fuzzy Case-Based Reasoning System

Bai

Zhang

et al. 2016

Applied Sciences

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Abstract:In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the "vague" concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.

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“…Fuzzy logic [1][2][3][4] and artificial neural networks (ANNs) [5][6][7][8] are important in the intelligent control of complex systems. A combination of them is widely used in solving classification, pattern recognition problems, and so on.…”

Section: Introductionmentioning

confidence: 99%

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Xue

Zhang

et al. 2014

Atmosphere

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Abstract:We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the "fuzzy rule-based neural network", which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the "neural fuzzy inference system", which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the "accurate" OPEN ACCESSAtmosphere 2014, 5 789 prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

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Stabilization of Rössler chaotic dynamical system using fuzzy logic control algorithm

Cited by 83 publications

References 48 publications

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

Fuzzy Case-Based Reasoning System

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

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