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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Smart Scheduling and Cost-Benefit Analysis of Grid-Enabled Electric Vehicles for Wind Power Integration
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A hybrid clustering and gradient descent approach for fuzzy modeling
Ching-Chang Wong,Chia-Chong Chen +1 more
TL;DR: A hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available.
Journal ArticleDOI
Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation
Guoying Liu,Yun Zhang,Aimin Wang +2 more
TL;DR: Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries, compared with five state-of-the-art image segmentation methods.
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Rough clustering of sequential data
TL;DR: The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group.
Journal ArticleDOI
Fuzzy Identification Based on a Chaotic Particle Swarm Optimization Approach Applied to a Nonlinear Yo-yo Motion System
TL;DR: Chaos particle swarm optimization algorithms, based on chaotic Zaslavskii map sequences, combined with efficient Gustafson-Kessel clustering algorithm are proposed here for the design of the premise part of production rules, while the least-mean-square technique is utilized for the subsequent part of the production rules of the TS fuzzy model.
References
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Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.