Share
Neuro-Fuzzy Clustering of Distorted Data Using Cat Swarm Optimization (in English)
Alina Shafronenko
(Author)
·
Yevgeniy Bodyanskiy
(Author)
·
Diana Rudenko
(Author)
·
LAP Lambert Academic Publishing
· Paperback
Neuro-Fuzzy Clustering of Distorted Data Using Cat Swarm Optimization (in English) - Shafronenko, Alina ; Bodyanskiy, Yevgeniy ; Rudenko, Diana
$ 36.28
$ 43.09
You save: $ 6.81
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Monday, June 24 and
Tuesday, June 25.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Neuro-Fuzzy Clustering of Distorted Data Using Cat Swarm Optimization (in English)"
Nowadays, computational intelligence technologies often and quite successfully are used in solving complex problems that, as a rule, do not have an analytical solution. Today, these technologies and especially artificial neural networks (ANN) are widely used to solve various problems of signal processing, optimization, optimal and adaptive control, pattern recognition, identification, time- series prediction, etc. At the same time, the described approaches to data recovery are workable only in cases when the initial data are set a priori, and the "object-property" table or time series has a fixed number of observations, i.e. do not change during processing. This book is devoted to the development and study of methods for dynamic data mining, containing missing and distorted observations. The main feature of data mining methods is to establish the presence and nature of hidden patterns in data, whereas traditional methods mainly deal with parametric evaluation of already established patterns.
