Analytical Study of Neural Network Techniques: SOM, MLP and Classifier-A Survey
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 3
Abstract
Abstract: This paper focuses on the various neural network techniques including Multilayer perceptron (MLP) neural network, classifier and self-organizing maps (SOMs). Various aspects of neural network techniques are mentioned in this paper along with the advantages and drawbacks. The neural network models are trained with measured values of the field strength at arbitrary points. The back propagation training algorithm is used for the learning process in MLP. The mechanism of supervised and unsupervised learning are also specified.
Authors and Affiliations
Sonali B. Wankhede
Keywords
Related Articles
- EP ID EP105163
- DOI 10.9790/0661-16378692
- Views 130
- Downloads 0
Intrusion Detection Techniques In Mobile Networks
The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. The recent denial of service attacks on major Internet sites have shown us, no o...
PATH PLANNING TECHNIQUES FOR MOBILE ROBOT: A REVIEW
The ability of a mobile robot to plan its path, avoid obstacles and optimize in static as well as dynamic environments is the key task in the field of robotics, which tends to find the shortest, collision free, optimal p...
An optimized link state routing protocol based on a cross layer design for wireless mesh network
Analysis of routing in Mesh Network reveals that Proactive routes are fast but suffers vulnerability of route failure under high mobility. Reactive routes on the other hand add extra overhead in the network for o...
SMS EncryptionUsing One-Time Pad Cipher
Abstract: The content of the SMS is very crucial. It consists of an imperative information. Unknown participants can not retrieve it. To protect the SMS content, the sender must provide the unique characterbefore it is...
Face Recognition using Feed Forward Neural Network
Abstract: In this paper, we propose four techniques for extraction of facial features namely 2DPCA, LDA, KPCA and KFA. The purpose of face feature extraction is to capture certain discriminative features that are unique...