Handbook of image processing operators
Handbook of image processing operators
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Fast Iris Detection for Personal Verification Using Modular Neural Nets
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Speeding-up normalized neural networks for face/object detection
Machine Graphics & Vision International Journal
A new fast forecasting technique using high speed neural networks
WSEAS Transactions on Signal Processing
Advanced technology for E-learning development
CIS'09 Proceedings of the international conference on Computational and information science 2009
Modular networks for active e-learning
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
User interface for internet applications
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
A novel high-speed neural model for fast pattern recognition
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Studying the efficiency of XML web services for real-time applications
SENSIG'09/VIS'09/MATERIALS'09 Proceedings of the 2nd WSEAS International Conference on Sensors, and Signals and Visualization, Imaging and Simulation and Materials Science
Fast packet detection by using high speed time delay neural networks
MUSP'10 Proceedings of the 10th WSEAS international conference on Multimedia systems & signal processing
A new approach for prediction by using integrated neural networks
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
A new expert system for pediatric respiratory diseases by using neural networks
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
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Prediction of market price is very important for strategic planning. In this paper, a new approach for fast market price prediction is presented. Such algorithm uses fast time delay neural networks (FTDNNs). The operation of these networks relies on performing cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented FTDNNs is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.