The partial solutions consideration based self-adaptive evolutionary algorithm: A learning structure of neuro-fuzzy networks

Hung, Pei-Chia; Lin, Shih‐Yen

doi:10.1016/j.eswa.2012.02.190

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Kayadelen (2011) studied the potential of genetic expression programming and an adaptive neuro-fuzzy computing paradigm to forecast the safety factor for liquefaction of soils. Hung and Lin (2012) developed a novel evolutionary algorithm named the partial solutions consideration based on a self-adaptive evolutionary algorithm (PSC-SEA) to adjust the parameters of a neuro-fuzzy network that was investigated in another paper by Wang et al (2012). Han et al (2012) proposed a selforganizing radial basis function NN model predictive control method for controlling the dissolved oxygen concentration in a wastewater treatment process (WWTP).…”

Section: Introductionmentioning

confidence: 99%

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A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems

Rastegar

Araújo

Mendes

2014

Journal of Vibration and Control

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This paper proposes a new unsupervised fuzzy clustering algorithm (NUFCA) to construct a novel online evolving Takagi–Sugeno (T-S) fuzzy model identification method and an adaptive predictive process control methodology. The proposed system identification approach consists of two main steps: antecedent T-S fuzzy model parameters identification and consequent parameters identification. The NUFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modelling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system; then the recursive least squares method is exploited to obtain initialization type consequent parameters and to construct a method for on-line fuzzy model identification. The integration of the proposed adaptive identification method with the generalized predictive control results in an effective adaptive predictive fuzzy control methodology. For better demonstration of the robustness and efficiency of the proposed methodology, it is applied to the identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment plant (WWTP); and to control a simulated continuous stirred tank reactor (CSTR), and a real experimental setup composed of two coupled DC motors. The results show that the developed evolving T-S fuzzy model methodology can identify nonlinear systems satisfactorily and can be successfully used for a prediction model of the process for the generalized predictive controller. It is also shown that the algorithm is robust to changes in the initial parameters, and to unexpected disturbances.

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Section: Introductionmentioning

confidence: 99%

“…Otherwise, the method must be robust enough to address the main important concerns in a control design. The methods proposed by Cazarez-Castro et al (2010), Kayadelen (2011), Hung and Lin (2012) and Han et al (2012) have the limitation of not being able to perform automatic selection of variables: pre-selection is performed.…”

Section: Introductionmentioning

confidence: 99%