Uncertain Data Analysis
Fuzzy Vector Algorithms
- Publisher's listprice GBP 155.00
-
74 051 Ft (70 525 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 10% (cc. 7 405 Ft off)
- Discounted price 66 646 Ft (63 473 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
74 051 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 23 April 2026
- ISBN 9781041060222
- Binding Hardback
- No. of pages124 pages
- Size 234x156 mm
- Language English
- Illustrations 76 Illustrations, black & white; 76 Line drawings, black & white; 25 Tables, black & white 700
Categories
Short description:
This book studies different classification, detection and decision fusion algorithms, and helps practitioners deal with uncertainty in their data sets.
MoreLong description:
This book studies different classification, detection, and decision fusion algorithms, and it helps practitioners deal with uncertainty in their data sets. Data uncertainties are considered as a collection of linguistic/fuzzy values or a vector of fuzzy numbers, and fuzzy algorithms are used to analyze these data sets. There are many theories and applications developed based on fuzzy set theory.
The topics of classification and prediction using fuzzy algorithms are introduced in the chapters on K-nearest prototype, clustering, and neural networks. The linguistic/fuzzy algorithm is designed to work with linguistic data represented by fuzzy vectors. The linguistic K-nearest prototypes algorithm is particularly useful in fields where data is inherently imprecise or fuzzy, such as in management questionnaire analysis, where responses may not be strictly quantitative. The reader also learns about clustering algorithms, such as linguistic hard C-means and linguistic fuzzy C-means, for single and multiple clusters, respectively. The book explores the integration of fuzzy multilayer perceptrons (FMLPs) with the cuckoo search (CS) algorithm to enhance the performance and applicability of neural networks in handling complex fuzzy data. The extended version of two commonly used fuzzy integrals covered include the Choquet and the Sugeno integrals. Mathematical analysis of these algorithms is included in the study of the different approaches each takes to the aggregation of uncertain data. Both integrals are powerful tools for handling fuzzy data, and their use in improving decision-making and analysis is demonstrated through real-world application examples using both of these algorithms. Very importantly, decision fusion is studied using fuzzy Dempster–Shafer theory with a real-world example of an application.
This book serves as a guide for practitioners, such as robotics engineers, computer scientists, and researchers working on computational intelligence. It is also suitable for graduate courses on fuzzy theories and fuzzy techniques.
MoreTable of Contents:
1. Linguistic/Fuzzy Vectors: What and Why?. 2. Linguistic K-Nearest Prototype. 3. Linguistic Clustering. 4. Fuzzy Multilayer Perceptrons. 5. Fuzzy Self-Organizing Feature Map. 6. Linguistic Fuzzy Integral. 7. Fuzzy Dempster’s Rule of Combination.
More
