Linguistic modeling by hierarchical systems of linguistic rules

Cordón, Óscar; Herrera, Francisco; Zwir, Igor

doi:10.1109/91.983275

Cited by 183 publications

(80 citation statements)

References 24 publications

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“…The HFRBCS approach [3] presents a more flexible KB structure that allows to improve the accuracy of the FRBCSs without losing their interpretability: the HKB, which is composed of a Hierarchical Data Base (HDB) and an HRB.…”

Section: Hierarchical Fuzzy Rule Based Classification Systemmentioning

confidence: 99%

“…We consider the modification of the Knowledge Base (KB) structure using the concept of "layers" that was introduced in [3], defined by the authors as Hierarchical Knowledge Base (HKB). In order to obtain a Hierarchical Fuzzy Rule Base Classification System (HFRBCS), we will apply a two-level learning methodology: 1) A Linguistic Rule Generation (LRG) method is used to create the initial Rule Base (RB), from which we extract the Hierarchical Rule Base (HRB).…”

Section: Introductionmentioning

confidence: 99%

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Analysing the Hierarchical Fuzzy Rule Based Classification Systems with genetic rule selection

Fernández

Jesus

Herrera

2010

2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

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Abstract-This contribution is focused on the enhancement of the precision for Fuzzy Rule Based Classification Systems by the refinement of the Knowledge Base. Specifically, we make use of a Hierarchical Fuzzy Rule Based Classification System, which consists in the application of a thicker granularity in order to generate the initial Rule Base, and to reinforce those problem subspaces that are specially difficult by means of the application of rules with a higher granularity. Furthermore, we will perform a genetic rule selection process in order to obtain a compact and accurate model.Our experimental results show the goodness of this approach, especially when the number of classes is high, which usually implies a higher difficulty in the separability of the examples. Our conclusions are supported by means of the corresponding statistical tests.

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Section: Hierarchical Fuzzy Rule Based Classification Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysing the Hierarchical Fuzzy Rule Based Classification Systems with genetic rule selection

Fernández

Jesus

Herrera

2010

2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

Self Cite

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“…Machine learning techniques -evolutionary computing and neural networks [51] and [15] have some similarities with some of the ant algorithms, especially ACO algorithms. Both evolutionary computing and the ACO met heuristic deploy a population of individuals to incrementally build more suitable solutions to a given problem, by building on the solutions of the previous populations.…”

Section: A Ant Algorithms As An Alternative To Other Machine Learninmentioning

confidence: 99%

“…There are some forms of specific evolutionary computing algorithms which are more close to ACO met heuristics. Characteristics [51] & [15]. Problem solved by Artificial Neural Network (ANN) [52], biologically inspired learning systems and AAs may be often represented graphically by a set of connecting nodes.…”

Section: A Ant Algorithms As An Alternative To Other Machine Learninmentioning

confidence: 99%

Bio-inspired Ant Algorithms: A review

Roy¹,

Chaudhuri²

2013

IJMECS

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Abstract─ Ant Algorithms are techniques for optimizing which were coined in the early 1990"s by M. Dorigo. The techniques were inspired by the foraging behavior of real ants in the nature. The focus of ant algorithms is to find approximate optimized problem solutions using artificial ants and their indirect decentralized communications using synthetic pheromones. In this paper, at first ant algorithms are described in details, then transforms to computational optimization techniques: the ACO metaheuristics and developed ACO algorithms. A comparative study of ant algorithms also carried out, followed by past and present trends in AAs applications. Future prospect in AAs also covered in this paper. Finally a comparison between AAs with well-established machine learning techniques were focused, so that combining with machine learning techniques hybrid, robust, novel algorithms could be produces for outstanding result in future.

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“…The data set has been taken from [9] . The data samples were divided into two sets labeled training and testing sets which are randomly selected from the whole sample as reported in [13] and [14] . As with other authors, 396 samples are used for training while the other 99 samples are used for testing.…”

Section: Estimation Of the Low Voltage Electrical Line Length And Maimentioning

confidence: 99%

Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice

Almaraashi

John

Hopgood

et al. 2016

Information Sciences

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a b s t r a c tThis paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in the modeling of four benchmark problems including real-world problems. The type-2 fuzzy logic system models are compared in their ability to model uncertainties associated with these problems. Issues related to this combination between simulated annealing and fuzzy logic systems, including type-2 fuzzy logic systems, are discussed. The results demonstrate that learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can add more capability to modeling than interval type-2 fuzzy logic systems. This finding can be seen as an important advance in type-2 fuzzy logic systems research and should increase the level of interest in the modeling applications of general type-2 fuzzy logic systems, despite their greater computational load.

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Linguistic modeling by hierarchical systems of linguistic rules

Cited by 183 publications

References 24 publications

Analysing the Hierarchical Fuzzy Rule Based Classification Systems with genetic rule selection

Analysing the Hierarchical Fuzzy Rule Based Classification Systems with genetic rule selection

Bio-inspired Ant Algorithms: A review

Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice

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