International Journal of Electronic Finance
ACO Optimizing Neural Network for Macroscopic Water Distribution System Modeling
ICICCI '10 Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics
Application of BP Network for Travel Behavior Analysis: Complexity Recognition of Trip Chaining
ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 01
Marginalized neural network mixtures for large-scale regression
IEEE Transactions on Neural Networks
Prediction of Typhoon Losses in the South-East of China Based on B-P Network
AICI '10 Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence - Volume 01
Technical Target Setting in QFD for Web Service Systems Using an Artificial Neural Network
IEEE Transactions on Services Computing
Neural-network design for small training sets of high dimension
IEEE Transactions on Neural Networks
The ensemble approach to neural-network learning and generalization
IEEE Transactions on Neural Networks
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In this paper, both truth and falsity inputs are used to trained neural networks. Falsity input is the complement of the truth input. Two pairs of neural networks are created. The first pair of neural networks are trained using the truth input whereas the second pair of neural networks are trained using the falsity input. Each pair of neural networks are trained to predict degree of truth and degree of falsity outputs based on the truth and falsity targets, respectively. Two novel techniques are proposed based on these two pairs of neural network. We experiment our proposed techniques to three classical benchmark data sets, which are housing, concrete compressive strength, and computer hardware from the UCI machine learning repository. It is found that our proposed techniques improve the prediction performance when compared to backpropagation neural network and complementary neural networks.