Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
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
A new fast forecasting technique using high speed neural networks
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
Fast detection of specific information in voice signal over internet protocol
WSEAS TRANSACTIONS on COMMUNICATIONS
Fast time delay neural networks for word detection in video conference
ECC'09 Proceedings of the 3rd international conference on European computing conference
Fast image matching on web pages
WSEAS Transactions on Signal Processing
Fast information retrieval from web pages
WSEAS Transactions on Information Science and Applications
Fast Time Delay Neural Networks for Detecting DNA Coding Regions
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
A novel high-speed neural model for fast pattern recognition
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fast word detection in a speech using new high speed time delay neural networks
WSEAS Transactions on Signal Processing
Fast harmonic current/voltage prediction by using high speed time delay neural networks
CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
New fast principal component analysis for real-time face detection
Machine Graphics & Vision International Journal
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
Prediction of market price by using fast time delay neural networks
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
An intelligent approach for fast detection of biological viruses in DNA sequence
ACELAE'11 Proceedings of the 10th WSEAS international conference on communications, electrical & computer engineering, and 9th WSEAS international conference on Applied electromagnetics, wireless and optical communications
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
A new hybrid system for information security
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
A new fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
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In this paper, a new neural model is presented. Fast Feedforwared Neural Networks (FFNNs) is integrated with modified recurrent neural networks for powerful estimation. The proposed new model is applied for prediction of power consumption. First, Modified Kohonen's Neural Networks (MKNNs) are used to facilitate the prediction process because they have the ability for clustering the input space into a number of classes. Therefore it is used for data classification to identify the categories which are essential for the prediction process. The unsupervised process performs the role of front-end data compression. For each category, the supervised learning algorithm LVQ is used for training FFNNs. The operation of FFNNs relies on performing cross correlation in the frequency domain between the input data and the weights of neural networks. Simulation results have shown that the presented integrated neural model is very powerful prediction.