Tag Archives: Fuzziness

Fuzzy Implications (Studies in Fuzziness and Soft Computing)

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Michal Baczynski, Balasubramaniam Jayaram "Fuzzy Implications (Studies in Fuzziness and Soft Computing)"
Springer | English | 2008-10-10 | ISBN: 3540690808 | 310 pages | PDF | 7 MB

Fuzzy Implications (Studies in Fuzziness and Soft Computing)
Fuzzy Implications (FIs) generalize the classical implication and play a similar important role in Fuzzy Logic (FL), both in FL_n and FL_w in the sense of Zadeh. Their importance in applications of FL, viz., Approximate Reasoning (AR), Decision Support Systems, Fuzzy Control (FC), etc., is hard to exaggerate. This treatise is perhaps the first attempt at dealing exclusively with this class of operations.
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Fuzzy Implications (Studies in Fuzziness and Soft Computing)

FREEDownload : Fuzzy Implications (Studies in Fuzziness and Soft Computing)

Michal Baczynski, Balasubramaniam Jayaram "Fuzzy Implications (Studies in Fuzziness and Soft Computing)"
Springer | English | 2008-10-10 | ISBN: 3540690808 | 310 pages | PDF | 7 MB

Fuzzy Implications (Studies in Fuzziness and Soft Computing)
Fuzzy Implications (FIs) generalize the classical implication and play a similar important role in Fuzzy Logic (FL), both in FL_n and FL_w in the sense of Zadeh. Their importance in applications of FL, viz., Approximate Reasoning (AR), Decision Support Systems, Fuzzy Control (FC), etc., is hard to exaggerate. This treatise is perhaps the first attempt at dealing exclusively with this class of operations.
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Optimal Models and Methods with Fuzzy Quantities

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Optimal Models and Methods with Fuzzy Quantities (Studies in Fuzziness and Soft Computing) by Bing-Yuan Cao
English | 2010-02-18 | ISBN: 3642107109 | PDF | 350 pages | 4 MB

Optimal Models and Methods with Fuzzy Quantities
The book contains ten chapters as follows, Prepare Knowledge, Regression and Self-regression Models with Fuzzy Coefficients; Regression and Self-regression Models with Fuzzy Variables, Fuzzy Input/output Model, Fuzzy Cluster Analysis and Fuzzy Recognition, Fuzzy Linear Programming, Fuzzy Geometric Programming, Fuzzy Relative Equation and Its Optimizing, Interval and Fuzzy Differential Equations and Interval and Fuzzy Functional and Their Variation.

It can not only be used as teaching materials or reference books for under-graduates in higher education, master graduates and doctor graduates in the courses of applied mathematics, computer science, artificial intelligence, fuzzy information process and automation, operations research, system science and engineering, and the like, but also serves as a reference book for researchers in these fields, particularly, for researchers in soft science.
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Soft Computing in Green and Renewable Energy Systems (Studies in Fuzziness and Soft Computing)

Kasthurirangan Gopalakrishnan, "Soft Computing in Green and Renewable Energy Systems (Studies in Fuzziness and Soft Computing)"
English | ISBN 10: 3642221750 | 2011 | PDF | 320 pages | 5.6 MB
Soft Computing in Green and Renewable Energy Systems provides a practical introduction to the application of soft computing techniques and hybrid intelligent systems for designing, modeling, characterizing, optimizing, forecasting, and performance prediction of green and renewable energy systems.

Research is proceeding at jet speed on renewable energy (energy derived from natural resources such as sunlight, wind, tides, rain, geothermal heat, biomass, hydrogen, etc.) as policy makers, researchers, economists, and world agencies have joined forces in finding alternative sustainable energy solutions to current critical environmental, economic, and social issues. The innovative models, environmentally benign processes, data analytics, etc. employed in renewable energy systems are computationally-intensive, non-linear and complex as well as involve a high degree of uncertainty.

Soft computing technologies, such as fuzzy sets and systems, neural science and systems, evolutionary algorithms and genetic programming, and machine learning, are ideal in handling the noise, imprecision, and uncertainty in the data, and yet achieve robust, low-cost solutions. As a result, intelligent and soft computing paradigms are finding increasing applications in the study of renewable energy systems. Researchers, practitioners, undergraduate and graduate students engaged in the study of renewable energy systems will find this book very useful.

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