Approximation Capabilities of Hierarchical Fuzzy Systems

Zeng, Xiao‐Jun; Keane, John

doi:10.1109/tfuzz.2005.856559

Cited by 89 publications

(39 citation statements)

References 31 publications

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“…The MOEA + Fuzzy approach uses a k-level hierarchical fuzzy system (HFS), which has been demonstrated to be a universal approximator [43]. The HFS is generated as a cascade of single fuzzy sub-systems.…”

Section: Fuzzy Function Approximationmentioning

confidence: 99%

Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

Ascia

Catania

Nuovo

et al. 2011

Applied Soft Computing

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Section: Fuzzy Function Approximationmentioning

confidence: 99%

Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

Ascia

Catania

Nuovo

et al. 2011

Applied Soft Computing

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“…On the basis of the experience with neural networks, it is advisable to deal with complex systems by decomposing them by choosing an overarching structure of various fuzzy inference systems (FISs) before data mining (Geman et al 1992). For modelling real-world problems, hierarchical FLMs have three advantages: interpretability, accuracy, and dimensionality reduction (Lee et al 2003;Liu and Li 2005;Zeng and Keane 2005).…”

Section: Fuzzy Logic Modellingmentioning

confidence: 99%

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

et al. 2010

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To reveal farmers' motives for on-farm diversification and integration of farming components in the Mekong Delta, Vietnam, we developed a fuzzy logic model (FLM) using a 10-step approach. Farmers' decision-making was mimicked in a three-layer hierarchical architecture of fuzzy inference systems, using data of 72 farms. The model includes three variables for family motives of diversification, six variables related to component integration, next to variables for the production factors and for farmers' appreciation of market prices and know-how on 10 components. To obtain a good classification rate of the less frequent activities, additional individual fine-tuning was necessary after general model calibration. To obtain the desired degree of sensitivity to each variable, it was necessary to use up to five linguistic values for some of the input and output variables in the intermediate hierarchical layers. Model's sensitivity to motivational variables determining diversification and integration was of the same magnitude as its sensitivity to market prices and farmers' know-how of the activities, but less than its sensitivity to labour, capital and land endowment. Modelling to support strategic decision-making seems too elaborate for individual farms, but FLM will be useful to integrate farmers' opinions in strategic decision-making at higher hierarchical levels.

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“…Fuzzy logic is used for many applications such as approximation [3][4], control systems [5][6], fuzzy classification [7], and fuzzy clustering [8]. In order to describe the nature and inherent phenomenon of complicated systems, fuzzy logic is used to model unknown dynamic systems [9][10]. In fuzzy logic, the validity of any statement is an issue of a specific degree, and is quantified using a Membership Function (MF).…”

Section: Introductionmentioning

confidence: 99%

Design of a hierarchical fuzzy model predictive controller

Fallah

Khanesar

Teshnehlab

2015

IJET

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In order to control a nonlinear system using Nonlinear Model Predictive Control (NMPC), a nonlinear model from system is required. In this paper, a hierarchical neuro-fuzzy model is used for nonlinear identification of the plant. The use of hierarchical neuro-fuzzy systems makes it possible to overcome the curse of dimensionality. In neuro-fuzzy systems, if the input number increases, then the number of rules increases exponentially. One solution to this problem is making use of Hierarchical Fuzzy System Mamdani (HFS) in which the number of the rules increases linearly. Gradient descent and recursive least square algorithm are used simultaneously to train the parameters of the HFS. Gradient Descent Algorithm is utilized to train the parameters, which appear nonlinearly in the output of HFS, and RLS is used to train the parameters of consequent the part, which appears linearly in the output of HFS. Finally, a model predictive fuzzy controller based on a predictive cost function is proposed. Using Gradient Descent Algorithm, the parameters of the controller are optimized. The proposed controller is simulated on the control of continuous stirred tank reactor. It is shown that the proposed method can control the system with high performance.

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Approximation Capabilities of Hierarchical Fuzzy Systems

Cited by 89 publications

References 31 publications

Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

Design of a hierarchical fuzzy model predictive controller

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