Title Details: | |
Artificial Neural Networks |
|
Authors: |
Kampourlazos, Vasileios Papakostas, Georgios |
Reviewer: |
Kechagias, Athanasios |
Subject: | MATHEMATICS AND COMPUTER SCIENCE > COMPUTER SCIENCE > INTELLIGENT SYSTEMS |
Description: | |
Abstract: |
After an introductory presentation of biological neural networks, basic concepts of neural computation are introduced, emphasizing parallel and distributed computing capabilities as well as learning abilities. The distinction between unsupervised and supervised learning is made. Linear algebra forms the basis for a detailed presentation of the simplest neural networks, Perceptrons, followed by more complex networks such as Backpropagation networks and the Delta learning rule. Subsequently, neural networks from Adaptive Resonance Theory originating from psychology are presented, along with self-organizing neural networks inspired by neurophysiology. Finally, contemporary trends are discussed, focusing on Spiking Neural Networks capable of processing temporal information.
|
Linguistic Editors: |
Violitzi, Georgia |
Type: |
Chapter |
Creation Date: | 2015 |
Item Details: | |
License: |
http://creativecommons.org/licenses/by-nc-nd/3.0/gr |
Handle | http://hdl.handle.net/11419/3444 |
Bibliographic Reference: | Kampourlazos, V., & Papakostas, G. (2015). Artificial Neural Networks [Chapter]. In Kampourlazos, V., & Papakostas, G. 2015. Introduction to Computational Intelligence [Undergraduate textbook]. Kallipos, Open Academic Editions. https://hdl.handle.net/11419/3444 |
Language: |
Greek |
Is Part of: |
Introduction to Computational Intelligence |
Publication Origin: |
Kallipos, Open Academic Editions |