The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
Transactions ID Number: 29-360
Full Name: POPESCU MARIUS-CONSTANTIN
Position: Associate Professor
Age: ON
Sex: Male
Address: CRAIOVA, STR. PALTINIS, NR. 59, BL. K-7, AP. 2
Country: ROMANIA
Tel: 0745438287
Tel prefix: 004
Fax: 0040251435255
E-mail address: popescu.marius.c@gmail.com
Other E-mails: popescu_ctin2006@yahoo.com
Title of the Paper: Multilayer Perceptron and Neural Networks
Authors as they appear in the Paper: MARIUS-CONSTANTIN POPESCU, LILIANA POPESCU, NIKOS MASTORAKIS
Email addresses of all the authors: popescu.marius.c@gmail.com, mastor@wses.org
Number of paper pages: 10
Abstract: The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning algorithm. Also called the generalized delta algorithm because it expands the training way of the adaline network, it is based on minimizing the difference between the desired output and the actual output, through the downward gradient method (the gradient tells us how a function varies in different directions). Training a multilayer perceptron is often quite slow, requiring thousands or tens of thousands of epochs for complex problems. The best known methods to accelerate learning are: the momentum method and applying a variable learning rate. The paper presents the possibility to control the induction driving using neural systems.
Keywords: Backpropagation algorithm, Gradient method, Multilayer perceptron, Induction driving.
EXTENSION of the file: .doc
Special (Invited) Session: The Backpropagation Algorithm Functions for the Multilayer Perceptron
Organizer of the Session: 622-083
How Did you learn about congress: abulucea@gmail.com, mircea.grigoriu@gmail.com
IP ADDRESS: 79.117.184.18
