Genetic Learning of Membership Functions for Mining Fuzzy Association Rules

Alcalá, Rafael; Alcalá‐Fdez, Jesús; Gacto, María José; Herrera, Francisco

doi:10.1109/fuzzy.2007.4295595

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…Regarding the definition of the number of fuzzy sets, it is possible to find proposals using clustering methods, such as the Fuzzy C-Means (Dunn 1973; Chen and Wang 1999;Alcalá et al 2007), genetic algorithms (Kaya and Alhajj 2004;Hong et al 2006), neural networks (Castellano et al 2005), entropy measure (Cheng and Chen 1997), fuzzy Kappa measure (Dou et al 2007), histograms (Medasani et al 1998), among others.…”

Section: • Defining the Number Of Fuzzy Setsmentioning

confidence: 99%

Data base definition and feature selection for the genetic generation of fuzzy rule bases

Cintra¹,

Monard²,

Camargo

2010

Evolving Systems

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This paper presents further investigations with a fuzzy genetic method for the generation of fuzzy rule bases using improved preprocessing conditions. This method, previously proposed by the authors, uses the degree of coverage of candidate rules to select the ones forming the search space of the genetic algorithm, thus it is named DOC-BASED method. For this particular fuzzy genetic method, a previous attribute selection might be necessary. The definition of the fuzzy data base can also impact the results of the automatic generation of rule bases. To this end, an heuristic method for database definition, FUZZY-DBD and an attribute selection method, FUZZY-WRAPPER, were previously proposed and investigated in different contexts by the authors. The goal of this paper is to propose and investigate an enhanced version of the DOC-BASED method, regarding time reduction for the genetic search space generation, combined with the FUZZY-DBD and the FUZZY-WRAP-PER methods for the database definition and attribute selection process. The methods used are described and the advantages of the enhanced version of the DOC-BASED method is discussed. Experiments were performed aiming at comparing results generated by the original plain DOC-BASED method and its extended version described here. The experiments also include studies using a filter-based attribute selection method. Experimental results on 10 datasets are presented and compared. Results show that a fuzzy approach to attribute selection and their proper fuzzification can yield significant improvements to rules generation.

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Section: • Defining the Number Of Fuzzy Setsmentioning

confidence: 99%

Data base definition and feature selection for the genetic generation of fuzzy rule bases

Cintra¹,

Monard²,

Camargo

2010

Evolving Systems

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“…The variable m is the gradient of the line equation while the variable C represents the intersection at the U axis (Donato and Barbieri, 1985;Alcalaa et al, 2007). The fuzzifier has one fuzzifier-entity and four ROM components labeled as 0, 1, 2 and ROM_3.…”

Section: Fuzzifiermentioning

confidence: 99%

Subway Train Braking System: A Fuzzy Based Hardware Approach

Reaz¹,

Jalil

Husain³

et al. 2011

American Journal of Applied Sciences

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Problem statement: Automated subway train-braking system require perfection, efficiency and fast response. In order to cope with this concerns, an appropriate algorithm need to be developed which need to be implemented in hardware for faster response. Approach: In this research, the FPGA realization of fuzzy based subway train braking system has been presented on an Alter FLEX10K device to provide an accurate and increased speed of convergence of the network. The fuzzy based subway train braking system is comprised of fusilier, inference, rule selector and defuzzifier modules. Sixteen rules are identified for the rule selector module. After determining the membership functions and its fuzzy variables, the Max-Min Composition method and Madman-Min implication operator are used for the inference module and the Centre of Gravity method is used for the defuzzification module. Each module is modeled individually using behavioral VHDL. The layers are then connected using structural VHDL. Two 8-bit and one 8-bit unsigned digital signals are used for input and output respectively. Six ROMs are defined in order to decrease the chances of processing and increasing the throughput of the system. Results: Functional simulations were commenced to verify the functionality of the individual modules and the system as well. We have validated the hardware implementation of the proposed approach through comparison, verification and analysis. The design has utilized 2372 units of LC with a system frequency of 139.8MHz. Conclusion: In this research, the FPGA realization of fuzzy brake system of subway train has been successfully implemented with minimum usage of logic cells. The validation study with C model shows that the hardware model is appropriate and the hardware approach shows faster and accurate response with full automatic control.

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“…At the next level Bilinear frequency warping functions are used which ensures computational simplicity. In our proposed method, fuzzy logic is applied to the amplitude scaling to improve the overall conversion performance [10]. The general block diagram of voice conversion is given in Fig.1.…”

Section: Introductionmentioning

confidence: 99%

Cepstral Domain Voice Conversion Based on Constrained Transformations

Indumathi¹,

Hewitt²,

Rajavel³

2015

IJERT

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This paper proposes a method for voice conversion in cepstral domain. The method basically involves two steps: Bilinear Frequency Warping and Amplitude Scaling. Frequency warping is done so that spectrum of source speaker is moved towards their image in the target speaker's spectrum. Amplitude scaling is done to compensate for the warping inaccuracy. The use of bilinear function is to warp the signal without any significant decrease in the quality scores. Fuzzy logic is applied to the amplitude scaling process in order to improve the perceptional quality. This method despite its simplicity, achieves similar performance scores to the previously available methods based on Gaussian Mixture Model.

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Genetic Learning of Membership Functions for Mining Fuzzy Association Rules

Cited by 12 publications

References 21 publications

Data base definition and feature selection for the genetic generation of fuzzy rule bases

Data base definition and feature selection for the genetic generation of fuzzy rule bases

Subway Train Braking System: A Fuzzy Based Hardware Approach

Cepstral Domain Voice Conversion Based on Constrained Transformations

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