The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 27-562
Full Name: Joseph Raj Vaidyanathan
Position: Associate Professor
Age: ON
Sex: Male
Address: Department of Computer Engineering, Faculty of Architecture and Engineering, European University of Lefke, TRNC, Mersin 10
Country: TURKEY
Tel: +90 533 836 27 19
Tel prefix:
Fax: +90 392 727 75 28
E-mail address: v_jose07@yahoo.co.in
Other E-mails: v.jose08@gmail.com
Title of the Paper: Better Learning of Supervised Neural Networks Based on Functional Graph - An Experimental Approach
Authors as they appear in the Paper: Joseph Raj V.
Email addresses of all the authors: v.jose08@gmail.com
Number of paper pages: 10
Abstract: Multilayered feed forward neural networks possess a number of properties which make them particularly suited to complex problems. Neural networks have been in use in numerous meteorological applications including weather forecasting. As Neural Networks are being more and more widely used in recent years, the need for their more formal definition becomes increasingly apparent. This paper presents a novel architecture of neural network models using the functional graph. The neural network creates a graph representation by dynamically allocating nodes to code local form attributes and establishing arcs to link them. The application of functional graph in the architecture of Electronic neural network and Opto-electronic neural network is detailed with experimental results. Learning is defined in terms of functional graph. The proposed architectures are applied in weather forecasting and X-OR problem. The weather forecasting has been carried out based on various factors!
consolidated from meteorological experts and documents. The inputs are temperature, air pressure, humidity, cloudiness, precipitation, wind direction, wind speed, etc., and outputs are heavy rain, moderate rain and no rain. The percentage of correctness of the weather forecasting of the conventional neural network models, functional graph based neural network models and the meteorological experts are compared.
Keywords: Back propagation, Convergence, Functional Graph, Neural network, Optical neuron, Weather forecasting
EXTENSION of the file: .doc
Special (Invited) Session: Better Performance of Neural Networks Using Functional Graph for Weather Forecasting
Organizer of the Session: 591-824
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IP ADDRESS: 212.175.151.227
