Optimization of Fuzzy C Means Clustering using Genetic Algorithm for an Image

Tyagi, Jyoti; Verma, Neeta

doi:10.5120/21634-4955

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…Based on the Gaussian output, the parameter center, c and sigma σ , mathematical model to construct fuzzy type 1 (FT1) trapMF is performed, as explained in [7]. Next, evaluation of the proposed method is done using a function called evalmf() in Matlab Fuzzy Logic Tool.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

“…Evaluate trapMF using evalMF( ) function FIS process in Algorithm 1 is repeated to generate IT2 trap FCM. Center, c and sigma, σ generated from the Gaussian output produce the UMF of the IT2 trap MF with four parameters a, b, c and d as described in [7]. Fig.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

“…The limitation of FCM is that it generates Gaussian MF, instead of linear MFs such as triangular and trapezoidal. The previous work proposed the generation of trapezoidal MF for the interval type 2 (IT2) fuzzy inference system (FIS) [7]. This paper proposes the extension of the work which is to tune the IT2 trapezoidal MF so that it could produce improved accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of Genetic Algorithm (GA) as part of producing the FIS is to generate IT2 trapezoidal MF (referred as trapMF in this paper) by using the theory of genetic tuning and lateral adjustment [8]. It will be the continuation of the previous work [7] where FT1 trapMF is constructed through the theory of Gaussian distribution. To generate IT2 trapMF, UMF and LMF of the MF need to be identified to get the range of the FOU.…”

Section: Introductionmentioning

confidence: 99%

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Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fouad¹,

Hassan²,

Salim³

2022

Computers, Materials &Amp; Continua

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Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.

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Section: Fuzzy Inference Systemmentioning

confidence: 99%

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fouad¹,

Hassan²,

Salim³

2022

Computers, Materials &Amp; Continua

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show abstract

“…Basic idea: FCM clustering algorithm is used to make each data in the data set quickly tend to its own extreme point, while genetic algorithm is used to get rid of the local minimum that data may fall into in the process of convergence [16]. Repeat the above operation until the optimal clustering result is obtained.…”

Section: B Ga-fcm Clustering Algorithmmentioning

confidence: 99%

Driving Behavior Clustering for Hazardous Material Transportation Based on Genetic Fuzzy C-Means Algorithm

Wang

2020

IEEE Access

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In recent years, many accidents of hazardous material transportation have been caused by dangerous driving behavior such as excessive speed or rapid speed change. To solve the problem, in this paper a series of indicators for driving behavior evaluation are selected for quantitative analysis based on the massive data of the operating vehicle networked control system. After that, three main indicators are introduced in detail, that is, the acceleration & deceleration behavior indicator, the over speed behavior indicator and the operation stability indicator. Then by using the GA-FCM clustering method, 40 drivers of hazardous material transportation are classified according to their behavior parameters. The results clearly show that the driving speed of the drivers with high-risk driving behavior, and most of the drivers with poor driving stability, are also at bad over speed level. Therefore, this paper creatively applies the method of combining factor analysis and GA-FCM clustering to the field of driving behavior of hazardous material transportation. It will facilitate enterprises and management departments to focus on controlling high-risk drivers, thereby reducing accidents. INDEX TERMS Driving behavior, factor analysis, genetic algorithm, fuzzy c-means clustering.

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A Network Intrusion Detection Method Based on Hybrid Rice Optimization Algorithm Improved Fuzzy C-Means

Jin

Wang

et al. 2018

2018 IEEE 4th International Symposium on Wireless Systems Within the International Conferences on Intelligent Data Acquisition

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Optimization of Fuzzy C Means Clustering using Genetic Algorithm for an Image

Cited by 5 publications

References 5 publications

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Driving Behavior Clustering for Hazardous Material Transportation Based on Genetic Fuzzy C-Means Algorithm

A Network Intrusion Detection Method Based on Hybrid Rice Optimization Algorithm Improved Fuzzy C-Means

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