Journal ArticleDOI
Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems
Sung-Kwun Oh,Witold Pedrycz +1 more
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TLDR
The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “IF …, THEN … ” statements, and exploits the theory of system optimization and fuzzy implication rules.About:
This article is published in Fuzzy Sets and Systems.The article was published on 2000-10-16. It has received 164 citations till now. The article focuses on the topics: Fuzzy classification & Fuzzy number.read more
Citations
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Polynomial neural networks architecture: analysis and design
TL;DR: A comparative analysis shows that the proposed PNN are models with higher accuracy than other fuzzy models and the essence of the design procedure dwells on the Group Method of Data Handling.
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The design of self-organizing polynomial neural networks
TL;DR: A class of neural architectures of Polynomial Neural Networks (PNNs) are introduced, a comprehensive design methodology is discussed, a series of numeric experiments are carried out and a polynomial type of mapping between input and output variables are realized.
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Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Babak Rezaee,M. H. Fazel Zarandi +1 more
TL;DR: Comparing the proposed approach with some other fuzzy systems and neural networks, it is shown that the developed TSK fuzzy system exhibits better results with higher accuracy and smaller size of architecture.
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A hierarchical fuzzy-clustering approach to fuzzy modeling
TL;DR: The proposed method for fuzzy modeling based on a hierarchical fuzzy-clustering scheme is successfully applied to three test examples, where the produced fuzzy models prove to be very accurate, as well as compact in size.
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On the use of the weighted fuzzy c-means in fuzzy modeling
TL;DR: A fuzzy clustering-based algorithm for fuzzy modeling that incorporates unsupervised learning with an iterative process into a framework, which is based on the use of the weighted fuzzy c-means, which proves to be very accurate as well as compact in size.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
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Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
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Time series analysis, forecasting and control
P. Young,S. Shellswell +1 more
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
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Time Series Analysis: Forecasting and Control
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An identification algorithm in fuzzy relational systems
TL;DR: An algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques is proposed.