Relevant bibliographies by topics / Fuzzy pattern / Journal articles
To see the other types of publications on this topic, follow the link: Fuzzy pattern.

Journal articles on the topic 'Fuzzy pattern'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Fuzzy pattern.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Shukhat, Boris. "Supervised fuzzy pattern recognition." Fuzzy Sets and Systems 100, no. 1-3 (November 1998): 257–65. http://dx.doi.org/10.1016/s0165-0114(97)00052-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Liren. "Optical Pattern Fuzzy Logic." Japanese Journal of Applied Physics 29, Part 2, No. 7 (July 20, 1990): L1281—L1283. http://dx.doi.org/10.1143/jjap.29.l1281.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Caulfield, H. John. "Fuzzy syntactical pattern recognition." Applied Optics 29, no. 17 (June 10, 1990): 2600. http://dx.doi.org/10.1364/ao.29.002600.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Dubois, Didier, Henri Prade, and Claudette Testemale. "Weighted fuzzy pattern matching." Fuzzy Sets and Systems 28, no. 3 (December 1988): 313–31. http://dx.doi.org/10.1016/0165-0114(88)90038-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Liu, Z. W., B. Wei, C. L. Kang, and J. W. Jiang. "THE IMPLEMENTATION OF HESITANT FUZZY SPATIAL CO-LOCATION PATTERN MINING ALGORITHM BASED ON PYTHON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 763–67. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-763-2020.

Full text
Abstract:
Abstract. As one of the important research directions in the spatial data mining, spatial co-location pattern mining aimed at finding the spatial features whose the instances are frequent co-locate in neighbouring domain. With the introduction of fuzzy sets into traditional spatial co-location pattern mining, the research on fuzzy spatial co-location pattern mining has been deepened continuously, which extends traditional spatial co-location pattern mining to deal with fuzzy spatial objects and discover their laws of spatial symbiosis. In this paper, the operation principle of a classical join-based algorithm for mining spatial co-location patterns is briefly described firstly. Then, combining with the definition of classical participation rate and participation degree, a novel hesitant fuzzy spatial co-location pattern mining algorithm is proposed based on the establishment of the hesitant fuzzy participation rate and hesitant fuzzy participation formula according to the characteristics in fusion of hesitant fuzzy set theory, the score function and spatial co-location pattern mining. Finally, the proposed algorithm is written and implemented based on Python language, which uses a NumPy system to the expansion of the open source numerical calculation. The Python program of the proposed algorithm includes the method of computing hesitant fuzzy membership based on score function, the implementation of generating k-order candidate patterns, k-order frequent patterns and k-order table instances. A hesitant fuzzy spatial co-location pattern mining experiment is carried out and the experimental results show that the proposed and implemented algorithm is effective and feasible.
APA, Harvard, Vancouver, ISO, and other styles
6

Ray, Kumar S. "Pattern Recognition Based on Fuzzy Set and Genetic Algorithm." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450009. http://dx.doi.org/10.1142/s0219467814500090.

Full text
Abstract:
In this paper, we consider a soft computing approach to pattern classification. Our basic tools for soft computing are fuzzy relational calculus (FRC) and genetic algorithm (GA). We introduce a new interpretation of multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. We also consider the notion of a fuzzy pattern vector to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one-dimensional fuzzy implication. For the estimation of Ri we use floating point representation of GA. Thus a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non-fuzzy features of a test pattern can be classified. The performance of the proposed scheme is tested on synthetic data. Subsequently, we use the proposed scheme for the vowel classification problem of an Indian language. Finally, a benchmark of performance is established by considering multiplayer perception (MLP), support vector machine (SVM) and the present method. The Abalone, Hosse Colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. This new tool for pattern classification is very effective for classification of patterns under vegue and imprecise environment. It can provide multiple classification under overlapped classes.
APA, Harvard, Vancouver, ISO, and other styles
7

Uehara, Kiyohiko, and Kaoru Hirota. "A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 2 (March 20, 2021): 213–25. http://dx.doi.org/10.20965/jaciii.2021.p0213.

Full text
Abstract:
A method is proposed for evaluating fuzzy rules independently of each other in fuzzy rules learning. The proposed method is named α-FUZZI-ES (α-weight-based fuzzy-rule independent evaluations) in this paper. In α-FUZZI-ES, the evaluation value of a fuzzy system is divided out among the fuzzy rules by using the compatibility degrees of the learning data. By the effective use of α-FUZZI-ES, a method for fast fuzzy rules learning is proposed. This is named α-FUZZI-ES learning (α-FUZZI-ES-based fuzzy rules learning) in this paper. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rules learning. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rules optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. Numerical results show that α-FUZZI-ES learning is superior to the exemplary conventional scheme in terms of accuracy and convergence speed when the evaluation function is non-differentiable.
APA, Harvard, Vancouver, ISO, and other styles
8

Phuong, Truong Duc, Do Van Thanh, and Nguyen Duc Dung. "Mining Fuzzy Sequential Patterns with Fuzzy Time-Intervals in Quantitative Sequence Databases." Cybernetics and Information Technologies 18, no. 2 (June 1, 2018): 3–19. http://dx.doi.org/10.2478/cait-2018-0024.

Full text
Abstract:
Abstract The main objective of this paper is to introduce fuzzy sequential patterns with fuzzy time-intervals in quantitative sequence databases. In the fuzzy sequential pattern with fuzzy time-intervals, both quantitative attributes and time distances are represented by linguistic terms. A new algorithm based on the Apriori algorithm is proposed to find the patterns. The mined patterns can be applied to market basket analysis, stock market analysis, and so on.
APA, Harvard, Vancouver, ISO, and other styles
9

Cheng, Bilian, Zheng Liu, Guang Chen, and Fengyuan Zou. "Generating cheongsam custom pattern based on fuzzy set theory." International Journal of Clothing Science and Technology 32, no. 5 (April 17, 2020): 725–41. http://dx.doi.org/10.1108/ijcst-06-2019-0086.

Full text
Abstract:
PurposeThe purpose of this paper is to quickly acquire a cheongsam pattern using the fit quantification method to meet individual fit requirement.Design/methodology/approachBased on the cheongsam pattern database including basic patterns and graded patterns, we defined the main control parts of the cheongsam pattern by analyzing the pattern modification. Combining human body shape characteristics, this paper utilized the fuzzy membership function to quantify the cheongsam fit, and defined the modified model of the cheongsam control part.FindingsThe fitness quantification method can provide suitable primary body characteristics for custom-pattern and helps to produce customized cheongsam quickly.Originality/valueThis paper proposed a method of generating customized cheongsam pattern based on fitness quantification by using fuzzy membership function. The method combined the industry pattern design experience and mathematic knowledge to generate the individual fit pattern rapidly. It can be applied in cheongsam customization.
APA, Harvard, Vancouver, ISO, and other styles
10

PAPAKOSTAS, G. A., Y. S. BOUTALIS, D. E. KOULOURIOTIS, and B. G. MERTZIOS. "FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 08 (December 2008): 1461–86. http://dx.doi.org/10.1142/s0218001408006910.

Full text
Abstract:
A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.
APA, Harvard, Vancouver, ISO, and other styles
11

ISHIBUCHI, Hisao, Ken NOZAKI, and Hideo TANAKA. "Pattern Classification by Fuzzy Rules." Journal of Japan Society for Fuzzy Theory and Systems 5, no. 1 (1993): 74–84. http://dx.doi.org/10.3156/jfuzzy.5.1_74.

Full text
APA, Harvard, Vancouver, ISO, and other styles
12

Hamilton-Wright, A., D. W. Stashuk, and H. R. Tizhoosh. "Fuzzy Classification Using Pattern Discovery." IEEE Transactions on Fuzzy Systems 15, no. 5 (October 2007): 772–83. http://dx.doi.org/10.1109/tfuzz.2006.889930.

Full text
APA, Harvard, Vancouver, ISO, and other styles
13

Bocklisch, Steffen F., and Franziska Bocklisch. "Fuzzy-Pattern-Klassifikatoren als Modelle." Informatik-Spektrum 38, no. 6 (October 21, 2015): 510–22. http://dx.doi.org/10.1007/s00287-015-0922-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
14

Shi, Y. F., L. H. He, and J. Chen. "Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy." International Journal of Intelligent Systems and Applications 1, no. 1 (October 18, 2009): 68–75. http://dx.doi.org/10.5815/ijisa.2009.01.08.

Full text
APA, Harvard, Vancouver, ISO, and other styles
15

Senge, Robin, Thomas Fober, Maryam Nasiri, and Eyke Hüllermeier. "Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung." at - Automatisierungstechnik 60, no. 10 (October 2012): 622–29. http://dx.doi.org/10.1524/auto.2012.1034.

Full text
APA, Harvard, Vancouver, ISO, and other styles
16

M., Inbavalli. "Fuzzy Inference Model for Computation and Prediction of Disease Pattern." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 672–79. http://dx.doi.org/10.5373/jardcs/v12sp4/20201533.

Full text
APA, Harvard, Vancouver, ISO, and other styles
17

Ouyang, Zhiping, Lizhen Wang, and Pingping Wu. "Spatial Co-Location Pattern Discovery from Fuzzy Objects." International Journal on Artificial Intelligence Tools 26, no. 02 (April 2017): 1750003. http://dx.doi.org/10.1142/s0218213017500038.

Full text
Abstract:
A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern mining (RCP), to mining co-location patterns at a membership threshold or within a membership range. For efficient SCP mining, we optimize the basic mining algorithm to accelerate the co-location pattern generation. To improve the performance of RCP mining, effective pruning strategies are developed to significantly reduce the search space. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments.
APA, Harvard, Vancouver, ISO, and other styles
18

Mukhlash, Imam, Desna Yuanda, and Mohammad Iqbal. "Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3374. http://dx.doi.org/10.11591/ijece.v8i5.pp3374-3385.

Full text
Abstract:
A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits.
APA, Harvard, Vancouver, ISO, and other styles
19

Ansari, Mohd Dilshad, Satya Prakash Ghrera, and Arunodaya Raj Mishra. "Texture Feature Extraction Using Intuitionistic Fuzzy Local Binary Pattern." Journal of Intelligent Systems 29, no. 1 (December 8, 2016): 19–34. http://dx.doi.org/10.1515/jisys-2016-0155.

Full text
Abstract:
Abstract In this paper, intuitionistic fuzzy local binary for texture feature extraction (IFLBP) has been proposed to encode local texture from the input image. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy sets in the representation of local patterns of texture in images. Intuitionistic fuzzy local binary pattern also contributes to more than one bin in the distribution of IFLBP values, which can further be used as a feature vector in the various fields of image processing. The performance of the proposed method has been demonstrated on various medical images and processing images of size 256×256. The obtained results validated the effectiveness and usefulness of our proposed method over the other reported methods, and new improvements are suggested.
APA, Harvard, Vancouver, ISO, and other styles
20

Zhao, Qi, Meng Zhang, Jia Li, and Yan Ru Chen. "Fuzzy Pattern Recognition of Steelmaking Process for Converter." Advanced Materials Research 97-101 (March 2010): 4461–65. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.4461.

Full text
Abstract:
A new method is proposed for recognizing patterns of the steelmaking process based on the light intensity from the mouth of converter. By analyzing the fuzzy theory and the measurement of variation characteristics of the light intensity, the fuzzy pattern recognition system is established. And a simulation with Matlab is then performed. The experimental results show that the fuzzy system can identify the prophase, metaphase and anaphase periods of the steelmaking effectively, and it provides a new method for the end-point auto-control of the converter steelmaking.
APA, Harvard, Vancouver, ISO, and other styles
21

Vantas, Konstantinos, and Epaminondas Sidiropoulos. "Intra-Storm Pattern Recognition through Fuzzy Clustering." Hydrology 8, no. 2 (March 25, 2021): 57. http://dx.doi.org/10.3390/hydrology8020057.

Full text
Abstract:
The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.
APA, Harvard, Vancouver, ISO, and other styles
22

Roh, Seok-Beom, Yong Soo Kim, and Tae-Chon Ahn. "Feature Selection of Fuzzy Pattern Classifier by using Fuzzy Mapping." Journal of Korean Institute of Intelligent Systems 24, no. 6 (December 25, 2014): 646–50. http://dx.doi.org/10.5391/jkiis.2014.24.6.646.

Full text
APA, Harvard, Vancouver, ISO, and other styles
23

Nakashima, Tomoharu, Yasuyuki Yokota, Hisao Ishibuchi, Gerald Schaefer, Aleš Drastich, and Michal Závišek. "Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (July 20, 2007): 546–53. http://dx.doi.org/10.20965/jaciii.2007.p0546.

Full text
Abstract:
We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.
APA, Harvard, Vancouver, ISO, and other styles
24

Ahn, Tae-Chon, Jin-Hyun Kang, Doo-Young Kang, and Yang-Woong Yoon. "Fuzzy Control of Elevator Speed Pattern." Journal of Korean Institute of Intelligent Systems 14, no. 7 (December 1, 2004): 857–64. http://dx.doi.org/10.5391/jkiis.2004.14.7.857.

Full text
APA, Harvard, Vancouver, ISO, and other styles
25

Rajasekaran, S., and G. A. Vijayalakshmi Pai. "Simplified Fuzzy ARTMAP as Pattern Recognizer." Journal of Computing in Civil Engineering 14, no. 2 (April 2000): 92–99. http://dx.doi.org/10.1061/(asce)0887-3801(2000)14:2(92).

Full text
APA, Harvard, Vancouver, ISO, and other styles
26

Guštin, V., and J. Virant. "Pattern recognition with fuzzy neural network." Microprocessing and Microprogramming 40, no. 10-12 (December 1994): 935–38. http://dx.doi.org/10.1016/0165-6074(94)90073-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
27

Meher, Saroj K. "Explicit rough–fuzzy pattern classification model." Pattern Recognition Letters 36 (January 2014): 54–61. http://dx.doi.org/10.1016/j.patrec.2013.09.002.

Full text
APA, Harvard, Vancouver, ISO, and other styles
28

Kulkarni, Arun, and Nikita kulkarni. "Fuzzy Neural Network for Pattern Classification." Procedia Computer Science 167 (2020): 2606–16. http://dx.doi.org/10.1016/j.procs.2020.03.321.

Full text
APA, Harvard, Vancouver, ISO, and other styles
29

Mouchaweh, Moamar Sayed, Arnaud Devillez, Gerard Villermain Lecolier, and Patrice Billaudel. "Incremental learning in Fuzzy Pattern Matching." Fuzzy Sets and Systems 132, no. 1 (November 2002): 49–62. http://dx.doi.org/10.1016/s0165-0114(02)00060-x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
30

Ray, Kumar S., and Jayati Ghoshal. "Neuro Fuzzy Approach to Pattern Recognition." Neural Networks 10, no. 1 (January 1997): 161–82. http://dx.doi.org/10.1016/s0893-6080(96)00056-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
31

ISHIBUCHI, Hisao, Ken NOZAKI, and Hideo TANAKA. "Pattern Recognition Using Distributed Fuzzy Rules." Transactions of the Institute of Systems, Control and Information Engineers 4, no. 12 (1991): 517–26. http://dx.doi.org/10.5687/iscie.4.517.

Full text
APA, Harvard, Vancouver, ISO, and other styles
32

Ray, K. S., and T. K. Dinda. "Pattern classification using fuzzy relational calculus." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 33, no. 1 (February 2003): 1–16. http://dx.doi.org/10.1109/tsmcb.2002.804361.

Full text
APA, Harvard, Vancouver, ISO, and other styles
33

Le, Kim. "Fuzzy relation compositions and pattern recognition." Information Sciences 89, no. 1-2 (February 1996): 107–30. http://dx.doi.org/10.1016/0020-0255(95)00231-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
34

Bailador, Gonzalo, and Gracián Triviño. "Pattern recognition using temporal fuzzy automata." Fuzzy Sets and Systems 161, no. 1 (January 2010): 37–55. http://dx.doi.org/10.1016/j.fss.2009.08.005.

Full text
APA, Harvard, Vancouver, ISO, and other styles
35

Peeva, K. "Fuzzy acceptors for syntactic pattern recognition." International Journal of Approximate Reasoning 5, no. 3 (May 1991): 291–306. http://dx.doi.org/10.1016/0888-613x(91)90014-d.

Full text
APA, Harvard, Vancouver, ISO, and other styles
36

Dutta Majumder, D. "Fuzzy sets in pattern recognition, image analysis and automatic speech recognition." Applications of Mathematics 30, no. 4 (1985): 237–54. http://dx.doi.org/10.21136/am.1985.104148.

Full text
APA, Harvard, Vancouver, ISO, and other styles
37

FALOMIR, ZOE, VICENT CASTELLÓ, M. TERESA ESCRIG, and JUAN CARLOS PERIS. "FUZZY DISTANCE SENSOR DATA INTEGRATION AND INTERPRETATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, no. 03 (June 2011): 499–528. http://dx.doi.org/10.1142/s0218488511007106.

Full text
Abstract:
An approach to distance sensor data integration that obtains a robust interpretation of the robot environment is presented in this paper. This approach consists in obtaining patterns of fuzzy distance zones from sensor readings; comparing these patterns in order to detect non-working sensors; and integrating the patterns obtained by each kind of sensor in order to obtain a final pattern that detects obstacles of any sort. A dissimilarity measure between fuzzy sets has been defined and applied to this approach. Moreover, an algorithm to classify orientation reference systems (built by corners detected in the robot world) as open or closed is also presented. The final pattern of fuzzy distances, resulting from the integration process, is used to extract the important reference systems when a glass wall is included in the robot environment. Finally, our approach has been tested in an ActivMedia Pioneer 2 dx mobile robot using the Player/Stage as the control interface and promising results have been obtained.
APA, Harvard, Vancouver, ISO, and other styles
38

Nair, Hema. "Summaries of certain spatial patterns retrieved from multidate remote-sensing data." Discrete Dynamics in Nature and Society 2004, no. 2 (2004): 287–300. http://dx.doi.org/10.1155/s1026022604402027.

Full text
Abstract:
This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as “Y isF” can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.
APA, Harvard, Vancouver, ISO, and other styles
39

Hu, Yi-Chung, Ruey-Shun Chen, Gwo-Hshiung Tzeng, and Jia-Hourng Shieh. "A Fuzzy Data Mining Algorithm for Finding Sequential Patterns." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 02 (April 2003): 173–93. http://dx.doi.org/10.1142/s0218488503002004.

Full text
Abstract:
Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.
APA, Harvard, Vancouver, ISO, and other styles
40

Hirota, Kaoru, Yoshinori Arai, and Yukiko Nakagawa. "Pattern Recognition & Image Understanding based on Fuzzy Technology." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (October 20, 1997): 71–78. http://dx.doi.org/10.20965/jaciii.1997.p0071.

Full text
Abstract:
Four image recognition and understanding techniques based on fuzzy technology developed by the authors group have been surveyed. First topics is a fuzzy clustering with additional data applied to the remote sensing images. It is modified version of the well known FCM. A robot arm and vision system on assembling line is presented using fuzzy discriminant tree for a real time use. The repetition method is introduced into the construction of discriminant tree. Third is the pattern recognition for a models of cars which is applied a fuzzy hierarchical pattern recognition based on fixation feedback. Finally, a fuzzy dynamic image understanding system is presented using fuzzy knowledge base and fuzzy inference method to understand dynamic image understanding on general roads in Japan. These techniques are mentioned the algorithms, and some of them are with experimental results.
APA, Harvard, Vancouver, ISO, and other styles
41

HU, YI-CHUNG, and FANG-MEI TSENG. "MINING SIMPLIFIED FUZZY IF-THEN RULES FOR PATTERN CLASSIFICATION." International Journal of Information Technology & Decision Making 08, no. 03 (September 2009): 473–89. http://dx.doi.org/10.1142/s021962200900348x.

Full text
Abstract:
A fuzzy if-then rule whose consequent part is a real number is referred to as a simplified fuzzy rule. Simplified fuzzy if-then rules have been widely used in function approximation problems due to no complicated defuzzification is required. The proposed simplified fuzzy rule-based classification system, whose number of output is equal to the number of different classes, approximates an unknown mapping from input to desired output for each discriminant function. Not only a fuzzy data mining method is proposed to find simplified fuzzy if-then rules from training data, but also the genetic algorithm is employed to determine some user-specified parameters. To evaluate the classification performance of the proposed method, computer simulations are performed on some well-known datasets, showing that the generalization ability of the proposed method is comparable to the other fuzzy or nonfuzzy methods.
APA, Harvard, Vancouver, ISO, and other styles
42

Kbir, M. Ait, H. Benkirane, K. Maalmi, and R. Benslimane. "Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules." Pattern Recognition Letters 21, no. 6-7 (June 2000): 503–9. http://dx.doi.org/10.1016/s0167-8655(00)00015-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
43

Khatibi, Vahid, and Gholam Ali Montazer. "Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition." Artificial Intelligence in Medicine 47, no. 1 (September 2009): 43–52. http://dx.doi.org/10.1016/j.artmed.2009.03.002.

Full text
APA, Harvard, Vancouver, ISO, and other styles
44

Gülbay, Murat, and Cengiz Kahraman. "Development of fuzzy process control charts and fuzzy unnatural pattern analyses." Computational Statistics & Data Analysis 51, no. 1 (November 2006): 434–51. http://dx.doi.org/10.1016/j.csda.2006.04.031.

Full text
APA, Harvard, Vancouver, ISO, and other styles
45

Ko, Tae Jo, and Dong Woo Cho. "Tool Wear Monitoring in Diamond Turning by Fuzzy Pattern Recognition." Journal of Engineering for Industry 116, no. 2 (May 1, 1994): 225–32. http://dx.doi.org/10.1115/1.2901934.

Full text
Abstract:
This paper introduces a fuzzy pattern recognition technique for monitoring single crystal diamond tool wear in the ultraprecision machining process. Selected features by which to partition the cluster of patterns were obtained by time series AR modeling of dynamic cutting force signals. The wear on a diamond tool edge appears to be classifiable into two types, micro-chipping and gradual, both very small compared to conventional tool wear. In this regard, we used a fuzzy technique in pattern recognition, which considers the ambiguity in classification as well as the weakness of the cutting force variation, to monitor the diamond tool wear status, with satisfactory results.
APA, Harvard, Vancouver, ISO, and other styles
46

Yao, Jin. "Researches on the Optimal Design of Intersection Signal Lights Fuzzy Control." Applied Mechanics and Materials 686 (October 2014): 109–12. http://dx.doi.org/10.4028/www.scientific.net/amm.686.109.

Full text
Abstract:
Fuzzy control is an important branch of intelligent control, and this paper describes the application of fuzzy pattern study in intelligent control, begins with a brief overview introduction for development of intelligent control, and then describes the main components of the fuzzy model and its characteristics and fuzzy pattern control system. Fuzzy pattern applications in the intersection signal intelligent control showed fuzzy mode control can effectively reduce the single intersection average vehicle delay time, making the intersection more unobstructed to pass.
APA, Harvard, Vancouver, ISO, and other styles
47

BROUWER, ROELOF K. "A FUZZY RECURRENT ARTIFICIAL NEURAL NETWORK (FRANN) FOR PATTERN CLASSIFICATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 05 (October 2000): 525–38. http://dx.doi.org/10.1142/s021848850000037x.

Full text
Abstract:
This paper proposes a recurrent neural network of fuzzy units, which may be used for approximating a hetero-associative mapping and also for pattern classification. Since classification is concerned with set membership, and objects generally belong to sets to various degrees, a fuzzy network seems a natural for doing classification. In the network proposed here each fuzzy unit defines a fuzzy set. The fuzzy unit in the network determines the degree to which the input vector to the unit lies in that fuzzy set. The fuzzy unit may be compared to a perceptron in which case the input vector is compared to the weighting vector associated with the unit by taking the dot product. The resulting membership value in case of the fuzzy unit is compared to a threshold. Training of a fuzzy unit is based on an algorithm for solving linear inequalities similar to the method used for Ho-Kashyap recording. Training of the whole network is done by training each unit separately. The training algorithm is tested by trying the algorithm out on representations of letters of the alphabet with their noisy versions. The results obtained by the simulation are very promising.
APA, Harvard, Vancouver, ISO, and other styles
48

Zhou, J., and S. Bennett. "A Supervised Learning Network Based on Adaptive Resonance Theory." International Journal of Neural Systems 08, no. 02 (April 1997): 239–46. http://dx.doi.org/10.1142/s0129065797000240.

Full text
Abstract:
A neural network architecture, fuzzy ART with logistic discrimination (ART-LD), is introduced as a method of realising the pattern recognition task in a supervised learning manner. The system is formed by the hierarchical organisation of two network modules: a fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART and a logistic discrimination. The learning consists of two separate stages. Firstly, the fuzzy ART module self-organises the input patterns into category clusters, whose operations are governed by fuzzy set theory and "competitive learning" dynamics that ensure fast and stable learning. Then the outputs, which can be interpreted as fuzzy memberships of an input pattern to the encoded categories, provide the spatial distance informtion that is generalised by the subsequent logistic discrimination to give the final prediction. Examples are presented, and the generalisation capabilities of ART-LD are demonstrated through two simulated and one real classification problem.
APA, Harvard, Vancouver, ISO, and other styles
49

D, Ravikumar, Dr Arun Raaza, and Dr V. Devi. "Optimization of fuzzy image pattern matching using genetic algorithm." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 526. http://dx.doi.org/10.14419/ijet.v7i2.33.14827.

Full text
Abstract:
The process of fuzzy image pattern recognizes object found in images by using the methods of fuzzy logic. Localization of object is al-so done. Fuzzy segmentation templates and operators, which fetch a large number of alternatives, constitute methods used in the method of fuzzy logic. Imperfect and imprecision of the input images and the templates images are in the consideration of fuzzy pattern matching and later incorporated in the matching process. This paper contemplates two methods one for fuzzy pattern and the other for the optimizing the matching scheme with a genetic algorithm. The process of optimization has its objective, in finding the location of reliable feature from a set of calibrated images through a simultaneous optimization of the templates and the segmentation function. Optimization has demonstrated and resulting a superior abstraction of the matches for an unobserved sample images and a good performance to the common method of pattern matching.
APA, Harvard, Vancouver, ISO, and other styles
50

Han, Lixin, Xiaoqin Zeng, and Hong Yan. "Fuzzy clustering analysis of microarray data." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 222, no. 7 (October 1, 2008): 1143–48. http://dx.doi.org/10.1243/09544119jeim384.

Full text
Abstract:
Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the bibliographies on the topic 'Fuzzy pattern' for other source types:
Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography