Most recent issue published online in the International Journal of Fuzzy Computation and Modelling. International Journal of Fuzzy Computation and Modelling http://www.inderscience.com/browse/index.php?journalID=423&year=2017&vol=2&issue=3 Inderscience Publishers Ltd en-uk support@inderscience.com International Journal of Fuzzy Computation and Modelling 2052-353X 2052-3548 © 2018 Inderscience Enterprises Ltd. © 2017 Inderscience Publishers Ltd editor@inderscience.com International Journal of Fuzzy Computation and Modelling https://www.inderscience.com/images/files/coverImgs/ijfcm_scoverijfcm.jpg http://www.inderscience.com/browse/index.php?journalID=423&year=2017&vol=2&issue=3 Solution of first order system of differential equation in fuzzy environment and its application http://www.inderscience.com/link.php?id=89437 In this paper, we solve a system of differential equation of first order in fuzzy environment using generalised Hukuhara derivative approach. Four different cases are discussed for the said differential equation: 1) coefficient is positive crisp number and initial condition is fuzzy number; 2) coefficient is negative crisp number and initial condition is fuzzy number; 3) coefficient is fuzzy number and initial condition is fuzzy number; 4) coefficient is interval number and initial condition is fuzzy number. Finally, we apply the results in Lanchaster combat model. The solution that comes in the application are defuzzified by removal area method. Solution of first order system of differential equation in fuzzy environment and its application
Sankar Prasad Mondal; Tapan Kumar Roy
International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 187 - 214
In this paper, we solve a system of differential equation of first order in fuzzy environment using generalised Hukuhara derivative approach. Four different cases are discussed for the said differential equation: 1) coefficient is positive crisp number and initial condition is fuzzy number; 2) coefficient is negative crisp number and initial condition is fuzzy number; 3) coefficient is fuzzy number and initial condition is fuzzy number; 4) coefficient is interval number and initial condition is fuzzy number. Finally, we apply the results in Lanchaster combat model. The solution that comes in the application are defuzzified by removal area method.

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10.1504/IJFCM.2017.089437 International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 187 - 214 Sankar Prasad Mondal Tapan Kumar Roy Department of Mathematics, National Institute of Technology, Agartala, Jirania - 799046,Tripura, India ' Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-711103, West Bengal, India fuzzy sets fuzzy differential equation Lanchaster combat model removal area method for defuzzification 2018-01-24T23:20:50-05:00 Copyright © 2018 Inderscience Enterprises Ltd. 2 3 187 214 2018-01-24T23:20:50-05:00
Robust type-2 fuzzy c-means load frequency controller for multi area (geothermal hydrothermal) interconnected power system with GRC http://www.inderscience.com/link.php?id=89439 Load frequency control (LFC) exerts a significant influence in determining the performance of an interconnected power system. The existing control strategies employed for LFC are mainly dependent on parameter, a variation which leads to poor performance of the system. The present paper proposes a new approach to investigate the LFC problem of a multi-area system considering generation rate constraints (GRC) implementing type-2 fuzzy system (T2FS). Type-2 fuzzy c-means clustering technique (T2FCM) is used for generation of optimised rule base. The proposed method is tested on three-area power system (first area with two geothermal - one hydro unit, second area with three thermal units and third area with two thermal - one hydro unit tied together) to illustrate its robust performance under different operating conditions. The result shows the performance of the proposed controller is much better than conventional controller and fuzzy c-means controller in terms of undershoot, overshoot and settling time. Robust type-2 fuzzy c-means load frequency controller for multi area (geothermal hydrothermal) interconnected power system with GRC
Anand Gondesi; R. Vijaya Santhi; K. Rama Sudha
International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 215 - 239
Load frequency control (LFC) exerts a significant influence in determining the performance of an interconnected power system. The existing control strategies employed for LFC are mainly dependent on parameter, a variation which leads to poor performance of the system. The present paper proposes a new approach to investigate the LFC problem of a multi-area system considering generation rate constraints (GRC) implementing type-2 fuzzy system (T2FS). Type-2 fuzzy c-means clustering technique (T2FCM) is used for generation of optimised rule base. The proposed method is tested on three-area power system (first area with two geothermal - one hydro unit, second area with three thermal units and third area with two thermal - one hydro unit tied together) to illustrate its robust performance under different operating conditions. The result shows the performance of the proposed controller is much better than conventional controller and fuzzy c-means controller in terms of undershoot, overshoot and settling time.

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10.1504/IJFCM.2017.089439 International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 215 - 239 Anand Gondesi R. Vijaya Santhi K. Rama Sudha Dept. of EE, A.U. College of Engineering College, Andhra University, India ' Dept. of EE, A.U. College of Engineering College, Andhra University, India ' Dept. of EE, A.U. College of Engineering College, Andhra University, India load frequency control LFC type-2 fuzzy system T2FS generation rate constraint GRC type-2 fuzzy c-means clustering T2FCM 2018-01-24T23:20:50-05:00 Copyright © 2018 Inderscience Enterprises Ltd. 2 3 215 239 2018-01-24T23:20:50-05:00
Interval optimality analysis in Erlang bulk arrival model in fuzzy environment http://www.inderscience.com/link.php?id=89445 In this paper, we propose a methodology to analyse the best arrival of K-phase Erlang distribution in fuzzy environment. We consider the queue parameters as triangular fuzzy numbers. At first, we construct the inverse membership of the steady state performance measures. Second, we construct the pairs of MINLP to calculate the lower and upper bound of the performance measure at the different possibility level of &lt;i&gt;α&lt;/i&gt; via Zadeh extension principle. We analyse the interval of the best arrival optimality using testing of hypothesis. A numerical example is illustrated. Interval optimality analysis in Erlang bulk arrival model in fuzzy environment
S. Bhuvaneswari; B. Rameshkumar; S. Murugesan
International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 240 - 251
In this paper, we propose a methodology to analyse the best arrival of K-phase Erlang distribution in fuzzy environment. We consider the queue parameters as triangular fuzzy numbers. At first, we construct the inverse membership of the steady state performance measures. Second, we construct the pairs of MINLP to calculate the lower and upper bound of the performance measure at the different possibility level of &lt;i&gt;α&lt;/i&gt; via Zadeh extension principle. We analyse the interval of the best arrival optimality using testing of hypothesis. A numerical example is illustrated.

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10.1504/IJFCM.2017.089445 International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 240 - 251 S. Bhuvaneswari B. Rameshkumar S. Murugesan Sree Sowdambika College of Engineering, Aruppukottai, Tamil Nadu, India ' Sree Sowdambika College of Engineering, Aruppukottai, Tamil Nadu, India ' Sri. S. Ramasamy Naidu Memorial College, Sattur, Tamil Nadu, India fuzzy set MINLP K-phase Erlang distribution chi-square distribution 2018-01-24T23:20:50-05:00 Copyright © 2018 Inderscience Enterprises Ltd. 2 3 240 251 2018-01-24T23:20:50-05:00
Some results on fuzzy 2-Banach spaces http://www.inderscience.com/link.php?id=89443 The notion of a fuzzy 2-Banach space is studied and examples are provided to illustrate the fuzzy 2-Banach space. Further, it is proved that every fuzzy 2-normed linear space of dimension 2 is a fuzzy 2-Banach space when the underlying field is complete. Some results on fuzzy 2-Banach spaces
A.R. Meenakshi; D. Cokilavany
International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 252 - 260
The notion of a fuzzy 2-Banach space is studied and examples are provided to illustrate the fuzzy 2-Banach space. Further, it is proved that every fuzzy 2-normed linear space of dimension 2 is a fuzzy 2-Banach space when the underlying field is complete.

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10.1504/IJFCM.2017.089443 International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 252 - 260 A.R. Meenakshi D. Cokilavany Department of Mathematics, Annamalai University, Annamalainagar - 608 002, India ' Department of Mathematics, Anna University, Chennai - 600 025, India fuzzy 2-norm bounded linear 2-functional continuous linear 2-functional fuzzy 2-Banach space 2018-01-24T23:20:50-05:00 Copyright © 2018 Inderscience Enterprises Ltd. 2 3 252 260 2018-01-24T23:20:50-05:00
Generalised intuitionistic fuzzy entropy and weighted correlation with application in multi-attributes decision-making http://www.inderscience.com/link.php?id=89446 The present paper introduces generalised entropy for intuitionistic fuzzy entropy of order &lt;i&gt;α&lt;/i&gt; with the evidences of its validity along with some of its properties. The proposed measure is a generalisation of the entropy given by De Luca and Termini (1972). It has been used to determine weights of both experts and attributes in intuitionistic fuzzy environment using decision matrices in multi-attributes decision-making problem with unknown weights. Further, weighted correlation coefficient is defined using the proposed entropy measure and correlation coefficients between alternatives and ideal point are determined. The value of correlation coefficient is used to rank the alternative and the alternative with greatest weighted correlation coefficient is selected as an optimal solution. Finally, an illustrative example describes its application on multi-attributes decision-making problem with undefined weights. Generalised intuitionistic fuzzy entropy and weighted correlation with application in multi-attributes decision-making
Pratiksha Tiwari
International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 261 - 274
The present paper introduces generalised entropy for intuitionistic fuzzy entropy of order &lt;i&gt;α&lt;/i&gt; with the evidences of its validity along with some of its properties. The proposed measure is a generalisation of the entropy given by De Luca and Termini (1972). It has been used to determine weights of both experts and attributes in intuitionistic fuzzy environment using decision matrices in multi-attributes decision-making problem with unknown weights. Further, weighted correlation coefficient is defined using the proposed entropy measure and correlation coefficients between alternatives and ideal point are determined. The value of correlation coefficient is used to rank the alternative and the alternative with greatest weighted correlation coefficient is selected as an optimal solution. Finally, an illustrative example describes its application on multi-attributes decision-making problem with undefined weights.

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10.1504/IJFCM.2017.089446 International Journal of Fuzzy Computation and Modelling, Vol. 2, No. 3 (2017) pp. 261 - 274 A.R. Meenakshi D. Cokilavany Delhi Institute of Advanced Studies, Plot No. 6, Sector 25, Rohini, Delhi, India intuitionistic fuzzy set IFS intuitionistic fuzzy entropy weights weighted correlation coefficient multi-attributes decision-making problem 2018-01-24T23:20:50-05:00 Copyright © 2018 Inderscience Enterprises Ltd. 2 3 261 274 2018-01-24T23:20:50-05:00