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978-981-13-1153-6 |
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20191022102548.0 |
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180717s2018 si | s |||| 0|eng d |
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|a 9789811311536
|9 978-981-13-1153-6
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|a 10.1007/978-981-13-1153-6
|2 doi
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|a QA76.9.M35
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|a 004.0151
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|a Recent Advances in Applications of Computational and Fuzzy Mathematics
|h [electronic resource] /
|c edited by Snehashish Chakraverty, Sanjeewa Perera.
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| 250 |
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|a 1st ed. 2018.
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|a Singapore :
|b Springer Singapore :
|b Imprint: Springer,
|c 2018.
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| 300 |
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|a XII, 178 p. 104 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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|a text file
|b PDF
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|a Chapter 1. Shallow Water Wave Equation with Fuzzy Parameters -- Chapter 2. ANN Based Solution of Static Structural Problem with Fuzzy Parameters -- Chapter3. Localization of Robots: Fuzzy Matrix Contractor Based Approach -- Chapter 4. Modeling Uncertain Radon Diffusion Equation in Soil: Fuzzy Polynomials Approach -- Chapter 5. Solving Fuzzy Static Structural Problems using Symmetric Group -- Chapter 6. Modeling Dispersal Risk of Invasive Alien Species -- Chapter 7. Developing a Decision Support Mathematical Model to Quantify the Air Quality.
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|a This book addresses the basics of interval/fuzzy set theory, artificial neural networks (ANN) and computational methods. It presents step-by-step modeling for application problems along with simulation and numerical solutions. In general, every science and engineering problem is inherently biased by uncertainty, and there is often a need to model, solve and interpret problems in the world of uncertainty. At the same time, exact information about models and parameters of practical applications is usually not known and precise values do not exist. This book discusses uncertainty in both data and models. It consists of seven chapters covering various aspects of fuzzy uncertainty in application problems, such as shallow water wave equations, static structural problems, robotics, radon diffusion in soil, risk of invasive alien species and air quality quantification. These problems are handled by means of advanced computational and fuzzy theory along with machine intelligence when the uncertainties involved are fuzzy. The proposed computational methods offer new fuzzy computing methods that help other areas of knowledge construction where inexact information is present.
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| 650 |
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|a Computer science-Mathematics.
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| 650 |
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|a Computational intelligence.
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| 650 |
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|a Math Applications in Computer Science.
|0 http://scigraph.springernature.com/things/product-market-codes/I17044
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| 650 |
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|a Computational Intelligence.
|0 http://scigraph.springernature.com/things/product-market-codes/T11014
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| 700 |
1 |
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|a Chakraverty, Snehashish.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Perera, Sanjeewa.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9789811311529
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| 776 |
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|i Printed edition:
|z 9789811311543
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| 776 |
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|i Printed edition:
|z 9789811345777
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| 856 |
4 |
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|u https://doi.org/10.1007/978-981-13-1153-6
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SCS
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| 950 |
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|a Computer Science (Springer-11645)
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