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Fuzzy model identification
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Hierarchical genetic fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Essentials of Fuzzy Modeling and Control
Essentials of Fuzzy Modeling and Control
A New Split and Merge Algorithm for Structure Identification in Takagi-Sugeno Fuzzy Model
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
Online identification of a neuro-fuzzy model through indirect fuzzy clustering of data space
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
Guest Editorial Evolving Fuzzy Systems–-Preface to the Special Section
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
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Linear trends of a time-varying process include useful and insight data about its temporal behaviors. In this paper, we introduce an approach for extracting the main linear trends of a nonlinear time-varying process. In this approach, originally, an adaptive linear model is utilized to estimate the temporal-linear trends of the process. Then, by using a suitable distance index, an online clustering algorithm has been developed to classify the estimated linear trends. Considering the mean and the number of members for each cluster, main linear trends are extracted for the process. Through an illustrative example, the methodology of the proposed approach in extracting main linear trends is explained and its capability is shown. Also, through two case studies -electrical load time series and pH neutralization process- the application of the proposed method in studying temporal behaviors of processes like stability, changing rate, oscillation and characteristics of transient states are explained.