Last edited by Kajind
Thursday, January 30, 2020 | History

5 edition of Fuzzy logic and neuroFuzzy applications in business and finance found in the catalog.

Fuzzy logic and neuroFuzzy applications in business and finance

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  • 6 Currently reading

Published by Prentice Hall PTR in Upper Saddle River, N.J .
Written in English

    Subjects:
  • Business -- Data processing.,
  • Finance -- Data processing.,
  • Fuzzy logic.,
  • Neural networks (Computer science)

  • Edition Notes

    Other titlesFuzzy logic & neuroFuzzy applications in business & finance
    StatementConstantin von Altrock.
    Classifications
    LC ClassificationsHF5548.2 .V66 1997
    The Physical Object
    Paginationx, 375 p. :
    Number of Pages375
    ID Numbers
    Open LibraryOL988758M
    ISBN 100135915120
    LC Control Number96026948

    Defuzzification Methods. ISRL, vol. These languages define some structures in order to include fuzzy aspects in the SQL statements, like fuzzy conditions, fuzzy comparators, fuzzy constants, fuzzy constraints, fuzzy thresholds, linguistic labels etc. Physica-Verlag Google Scholar 8. This is a preview of subscription content, log in to check access.

    Early applications[ edit ] Many of the early successful applications of fuzzy logic were implemented in Japan. Artificial Bee Colony algorithm Artificial Bee Colony ABC is a swarm intelligence-based algorithm which is inspired by the intelligent behavior of honey bees. Nowadays the new theories of soft computing are used for these purposes. Bernice, C. The objective of this special issue is to explore the advances of fuzzy logic in a large number of real-life applications and commercial products in a variety of fields. References Abel, H.

    In order to solve this, an extension of the notions of fuzzy grammar and fuzzy Turing machine are necessary. Conventional Control. Lotfi A. References 1. Proponents[ who? Moreover, other than rule-base minimization issue, training the parameters of ANFIS model is one of the main issues encountered when the model is applied to the real-world problems.


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Fuzzy logic and neuroFuzzy applications in business and finance book

Fuzzy databases[ edit ] Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. This algorithm has a diverse range of industrial applications. FML allows modelling a fuzzy logic system in a human-readable and hardware independent way.

Zadeh argues that fuzzy logic is different in character from probability, and is not a replacement for it.

Also, it provides an effective means for conflict resolution of multiple criteria and better assessment of options. No formulas, no complex math, just everything you need for a hands-on start.

The proposed approach to achieve the goal of this study is further explained in next section which defines research methodology. The book also contains an "Internet Resource Page" to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc.

Some of the essential characteristics of fuzzy logic relate to the following []. Annotation c. Bosc et al. Data partitioning ANFIS can be constructed by partitioning of the input-output data into rule patches.

The problem of assessing the quality of fuzzy data is a difficult one. Data Clustering. This branch of mathematics has instilled new life into scientific fields that have been dormant for a long time. Fuzzy logic allows for the inclusion of vague human assessments in computing problems.

Since then, the ABC algorithm has been used infields such as data mining, image processing and numerical problems. Springer Google Scholar 7.

Probability [24] that probability theory is a subtheory of fuzzy logic, as questions of degrees of belief in mutually-exclusive set membership in probability theory can be represented as certain cases of non-mutually-exclusive graded membership in fuzzy theory.

The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field.

Ribeiro, R. AISC, vol. A simple model is proposed to calculate recommendations for investors. Product fuzzy logic is the extension of basic fuzzy logic BL where conjunction is product t-norm.The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control.

The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services.

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Part II: Applications of Fuzzy Set Theory Fuzzy Logic Classical Logics Revisited Linguistic Truth Tables Approximate and Plausible Reasoning Fuzzy Languages Support Logic Programming and Fril Customer Segmentation in Banking and Finance Fuzzy Logic and NeuroFuzzy Applications in Business and Finance By Constantin von Altrock Published Nov 18, by Prentice Hall.

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Y. Atinc, and A. Kursat, Comparison with sugeno model and measurement of cancer risk analysis by new fuzzy logic approach, African Journal of Biotechnology, vol.

11, no. 4, pp.[7] H. Matoussi, and A. Abdelmoula, Using a neural network-based methodology for credit risk evaluation of a Tunisian bank, Middle Eastern Finance and.