Journal ArticleDOI
Structure identification of generalized adaptive neuro-fuzzy inference systems
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TLDR
This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs), and a new criterion called structure identification criterion (SIC) is proposed that deals with a trade off between performance and computational complexity of the GANFIS model.Abstract:
This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using the fuzzy curve. For structure identification, a new criterion called structure identification criterion (SIC) is proposed. SIC deals with a trade off between performance and computational complexity of the GANFIS model. The computational complexity of gradient descent learning is formulated based on simulation study. Three methods of initialization of GANFIS, viz., fuzzy curve, fuzzy C-means in x/spl times/y space and modified mountain clustering have been compared in terms of cluster validity measure, Akaike's information criterion (AIC) and the proposed SIC.read more
Citations
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SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network
TL;DR: A new network is proposed, in which unidimensional membership functions are used, and only two parameters for each rule are employed, thus reducing the number of parameters.
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Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey
Igor Škrjanc,Jose Antonio Iglesias,Araceli Sanchis,Daniel Leite,Edwin Lughofer,Fernando Gomide +5 more
TL;DR: This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally.
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Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process
TL;DR: The idea of using an evolving method as a base for the fault-detection/monitoring system is tested and the results indicate the potential improvement of the WWTP's control during a sensor malfunction.
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High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach
TL;DR: In this paper, the use of a neuro-fuzzy-based machine learning method for predicting the high cycle fatigue life of laser powder bed fusion stainless steel 316L was examined.
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Recursive clustering based on a Gustafson–Kessel algorithm
Dejan Dovžan,Igor Škrjanc +1 more
TL;DR: In this paper an on-line fuzzy identification of Takagi Sugeno fuzzy model is presented and the method is used to develop an adaptive fuzzy predictive functional controller for a pH process.
References
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TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
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