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# Fuzzy Interval Matrices, Neutrosophic Interval Matrices and Their Applications

## By Smarandache, Florentin

Book Id: WPLBN0002828312
Format Type: PDF (eBook)
File Size: 1.04 mb
Reproduction Date: 7/23/2013

 Title: Fuzzy Interval Matrices, Neutrosophic Interval Matrices and Their Applications Author: Smarandache, Florentin Volume: Language: English Subject: Collections: Historic Publication Date: 2013 Publisher: World Public Library Member Page: Florentin Smarandache Citation APA MLA Chicago Smarandache, F., & Vasantha Kandasamy, W. B. (2013). Fuzzy Interval Matrices, Neutrosophic Interval Matrices and Their Applications. Retrieved from http://self.gutenberg.org/

Description
The new concept of fuzzy interval matrices has been introduced in this book for the first time. The authors have not only introduced the notion of fuzzy interval matrices, interval neutrosophic matrices and fuzzy neutrosophic interval matrices but have also demonstrated some of its applications when the data under study is an unsupervised one and when several experts analyze the problem.

Summary
This book has three chapters. The first chapter is introductory in nature and makes the book a self-contained one. Chapter two introduces the concept of fuzzy interval matrices. Also the notion of fuzzy interval matrices, neutrosophic interval matrices and fuzzy neutrosophic interval matrices, can find applications to Markov chains and Leontief economic models. Chapter three gives the application of fuzzy interval matrices and neutrosophic interval matrices to real-world problems by constructing the models already mentioned. Further these models are mainly useful when the data is an unsupervised one and when one needs a multi-expert model. The new concept of fuzzy interval matrices and neutrosophic interval matrices will find their applications in engineering, medical, industrial, social and psychological problems.

Excerpt
1.2 Definition of Fuzzy Cognitive Maps In this section we recall the notion of Fuzzy Cognitive Maps (FCMs), which was introduced by Bart Kosko in the year 1986. We also give several of its interrelated definitions. FCMs have a major role to play mainly when the data concerned is an unsupervised one. Further this method is most simple and an effective one as it can analyse the data by directed graphs and connection matrices. DEFINITION 1.2.1: An FCM is a directed graph with concepts like policies, events etc. as nodes and causalities as edges. It represents causal relationship between concepts. Example 1.2.1: In Tamil Nadu (a southern state in India) in the last decade several new engineering colleges have been approved and started. The resultant increase in the production of engineering graduates in these years is disproportionate with the need of engineering graduates.