Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques

2010 ◽  
Vol 180 (24) ◽  
pp. 4772-4783 ◽  
Author(s):  
Shyi-Ming Chen ◽  
Yu-Chuan Chang
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Rule ◽  
Interpolation Techniques

Special Issue on Advances in Fuzzy Inference and its Related Techniques

2013 ◽  
Vol 17 (1) ◽  
pp. 43-43
Author(s):  
Kiyohiko Uehara
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Financial Institution ◽  
Fuzzy Rule ◽  
Large Data ◽  
Industrial Applications ◽  
Editorial Office ◽  
Special Issue ◽  
Inference Systems

Fuzzy inference provides a way to describe system behavior using humanly understandable rules. Based on this advantage, fuzzy inference has been applied in a wide variety of fields, including control, prediction, and pattern recognition. It has also had a corresponding impact on industrial applications. The four articles included in this special issue cover the advances made in fuzzy inference and related techniques. The first paper proposes a method for fuzzy rule interpolation on the basis of the generalized mean. This method makes it possible to perform nonlinear mapping of convex fuzzy sets even with sparse fuzzy rules. The second paper proposes a fuzzy clustering algorithm, landmark fuzzy neighborhood DBSCAN (landmark FN-DBSCAN). This algorithm is quite efficient in the clustering of large data sets, particularly compared to conventional density-based algorithms. Fuzzy clustering can be used to construct fuzzy rule bases. The third paper applies fuzzy inference to ultrasonic human brain imaging based on YURAGI synthesis. In this method, the thickness of bones is calculated effectively from synthesized waves using fuzzy inference. The fourth paper applies adaptive neurofuzzy inference systems (ANFIS) to financial institution failure prediction. These systems function effectively in the handling of the finance data of Thai firms with high nonlinearity. As a guest editor, I really appreciate the efforts of the contributors and reviewers. I am very grateful to staffs in JACIII editorial office for their kind support.

From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering

2021 ◽  
pp. 1-10
Author(s):  
Kaijie Xu ◽  
Hanyu E ◽  
Yinghui Quan ◽  
Ye Cui ◽  
Weike Nie
Keyword(s):  
Design Process ◽  
Fuzzy Clustering ◽  
Mathematical Proof ◽  
Experimental Studies ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Clustering Method ◽  
Rule Based ◽  
Information Granules ◽  
Fuzzy C Means

In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme.

scholarly journals Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Jean Marie Vianney Kinani ◽  
Alberto Jorge Rosales Silva ◽  
Francisco Gallegos Funes ◽  
Dante Mújica Vargas ◽  
Eduardo Ramos Díaz ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Brain Lesion ◽  
Fuzzy Rule ◽  
Computation Time ◽  
Detection Algorithm ◽  
Lesion Detection ◽  
Fluid Attenuated Inversion Recovery ◽  
Level Set Evolution

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

A Constructive Method for Building Fuzzy Rule-Based Systems

2003 ◽  
Vol 11 (02) ◽  
pp. 217-233 ◽  
Author(s):  
Ignacio Requena ◽  
Armando Blanco ◽  
Miguel Delgado
Keyword(s):  
Fuzzy Clustering ◽  
Selection Process ◽  
Fuzzy Rule ◽  
Constructive Method ◽  
Rule Based ◽  
Fuzzy Membership Functions ◽  
Empirical Performance ◽  
Stage Process ◽  
Linguistic Labels ◽  
Rule Based Systems

This paper proposes a new method for identifying unknown systems with Fuzzy Rule-Based Systems (FRBSs). The method employs different methodologies from the discipline of Soft Computing (Artificial Neural Networks, Fuzzy Clustering) and follows a three-stage process. Firstly, the structure of the FRBS rules is determined using a feature selection process. A fuzzy clustering procedure is then used to establish the number of fuzzy rules. In the third step, the fuzzy membership functions are constructed for the linguistic labels. Finally, the empirical performance of the algorithm is studied by applying it to a number of classification and approximation problems.

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