Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Neural Networks for Intelligent Signal Processing (Series on Innovative Intelligence, Vol. 4)
Neural Networks for Intelligent Signal Processing (Series on Innovative Intelligence, Vol. 4)
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
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In regression neural networks for pattern recognition of preprocessed guided waves signals in beams, a trained network produced large errors when identifying a test pattern not found in the training set. To improve the accuracy of results, a new neural network procedure was introduced where progressive training was performed in a series combined network with the integration of a weight-range selection (WRS) technique that was dependent on the test pattern. The WRS method was applied for a supervised multi-layer perceptron operating with one hidden layer of neurons and trained using a backpropagation algorithm. The system was able to achieve average predictions accurate to 2.5% and 7.8% of the original training range sizes for the damage location and depth, respectively, while the WRS provided up to 13.9% improvement compared to equivalent conventional neural networks.