The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Visualization of Neuro-Fuzzy Networks Training Algorithms: The Backpropagation Algorithm Approach
Abstract
The fusion of Artificial Neural Networks and Fuzzy Logic Systems allows researchers to model real world problems through the development of intelligent and adaptive systems. Artificial Neural networks are able to adapt and learn by adjusting the interconnections between layers while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as “Neuro-Fuzzy” systems. In this chapter, the basic elements of both approaches are analyzed while neuro-fuzzy networks learning algorithms are presented. Here, we combine the use of neuro-fuzzy algorithms with multimedia-based signals for training. Ultimately this process may be employed for automatic identification of patterns introduced in medical applications and more specifically for analysis of content produced by brain imaging processes.
Related Content
|
Peter Arthur Barone.
© 2023.
17 pages.
|
|
Patricia A. Goforth.
© 2023.
22 pages.
|
|
Steven Lloyd Leeper.
© 2023.
18 pages.
|
|
Neslihan Yayla.
© 2023.
25 pages.
|
|
İlknur Gümüş.
© 2023.
14 pages.
|
|
Sarah E. Daly.
© 2023.
15 pages.
|
|
Yakup Alper Varış.
© 2023.
22 pages.
|
|
|