Supervised fuzzy partitioning

This paper proposes a new approach to extracting natural strokes from the skeletons of loosely-constrained, off-line handwritten Chinese characters. It admits the output substrokes from a previously proposed fuzzy substroke extractor as its inputs. By identifying a number of expected ambiguities which include mutual similarities, unstable touches and joint/cross distortions, fuzzy stroke models are constructed and a "hit-all" fuzzy stroke matching strategy is pursued. Fuzzy partitioning technique is used to generate a ranked list of consistent stroke sets from the set of fuzzy strokes being identified. With this approach, a maximum of 20 distinct natural stroke classes can be extracted from each input character, together with an estimate on the actual count of strokes which compose the character. Our system offers a number of performance tuning capabilities such as the computation of the fuzzy scores of each extracted stroke, the adjustment on the fuzzy stroke model parameters, and the potential of incorporating one's personal writing styles into our methodology.

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INDIRECT ADAPTIVE CONTROL OF UNKNOWN MULTI VARIABLE NONLINEAR SYSTEMS WITH PARAMETRIC AND DYNAMIC UNCERTAINTIES USING A NEW NEURO-FUZZY SYSTEM DESCRIPTION

International Journal of Neural Systems ◽

10.1142/s0129065710002310 ◽

2010 ◽

Vol 20 (02) ◽

pp. 129-148 ◽

Cited By ~ 35

Author(s):

DIMITRIOS THEODORIDIS ◽

YIANNIS BOUTALIS ◽

MANOLIS CHRISTODOULOU

Keyword(s):

Dynamical Systems ◽

Nonlinear Dynamical Systems ◽

High Order ◽

Control Signal ◽

Parameter Uncertainties ◽

Fuzzy Partitioning ◽

Neuro Fuzzy ◽

Square Systems ◽

System States ◽

Dynamic Uncertainties

The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks.

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Prediction of oral bioavailability by adaptive fuzzy partitioning

European Journal of Medicinal Chemistry ◽

10.1016/s0223-5234(03)00052-7 ◽

2003 ◽

Vol 38 (4) ◽

pp. 427-431 ◽

Cited By ~ 24

Author(s):

M Pintore

Keyword(s):

Oral Bioavailability ◽

Adaptive Fuzzy ◽

Fuzzy Partitioning

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Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach

Medical & Biological Engineering & Computing ◽

10.1007/s11517-016-1508-7 ◽

2016 ◽

Vol 55 (1) ◽

pp. 101-115 ◽

Cited By ~ 51

Author(s):

Sk. Saddam Ahmed ◽

Nilanjan Dey ◽

Amira S. Ashour ◽

Dimitra Sifaki-Pistolla ◽

Dana Bălas-Timar ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Disease Classification ◽

Fuzzy Partitioning ◽

Neuro Fuzzy

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Distinguishing between two Antarctic species of Eocene Palaeeudyptes penguins: a statistical approach using tarsometatarsi

Polish Polar Research ◽

10.2478/popore-2013-0020 ◽

2013 ◽

Vol 34 (3) ◽

pp. 237-252 ◽

Cited By ~ 12

Author(s):

Piotr Jadwiszczak ◽

Carolina Acosta Hospitaleche

Keyword(s):

Statistical Approach ◽

Finite Mixture Model ◽

Species Boundaries ◽

Size Distributions ◽

Antarctic Species ◽

Clustering Techniques ◽

Significant Challenge ◽

Principal Coordinates ◽

Fuzzy Partitioning ◽

The Antarctic

AbstractDefining species boundaries, due to morphological variation, often represents a significant challenge in paleozoology. In this paper we report results from multi− and univariate data analyses, such as enhanced clustering techniques, principal coordinates or− dination method, kernel density estimations and finite mixture model analyses, revealing some morphometric patterns within the Eocene Antarctic representatives of Palaeeudyptes penguins. These large−sized birds were represented by two species, P. gunnari and P. klekowskii, known mainly from numerous isolated bones. Investigations focused on tarso− metatarsi, crucial bones in paleontology of early penguins, resulted in a probability−based framework allowing for the “fuzzy” partitioning the studied specimens into two taxa with partly overlapping size distributions. Such a number of species was supported by outcomes from both multi− and univariate studies. In our opinion, more reliance should be placed on the quantitative analysis of form when distinguishing between species within the Antarctic Palaeeudyptes.

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Enhancing the Performance of K-Means Clustering by using Fuzzy Partitioning Matrix

International Journal of Computer Applications ◽

10.5120/ijca2017913996 ◽

2017 ◽

Vol 166 (4) ◽

pp. 18-24

Author(s):

Ahtesham Husain ◽

Manoj E.

Keyword(s):

Fuzzy Partitioning

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A HEDGE ALGEBRAS BASED CLASSIFICATION REASONING METHOD WITH MULTI-GRANULARITY FUZZY PARTITIONING

Journal of Computer Science and Cybernetics ◽

10.15625/1813-9663/35/4/14348 ◽

2019 ◽

Vol 35 (4) ◽

pp. 319-336

Author(s):

Phạm Đình Phong ◽

Nguyen Duc Du ◽

Nguyen Thanh Thuy ◽

Hoang Van Thong

Keyword(s):

Fuzzy Sets ◽

Fuzzy Set ◽

Data Classification ◽

Design Methods ◽

Fuzzy Classification ◽

Classification Rule ◽

Linguistic Terms ◽

Fuzzy Partitioning ◽

Fuzzy Partitions ◽

Rule Bases

During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results.

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