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Neural Networks
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Networks and Expert Systems
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Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
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Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering
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Integrated Computer-Aided Engineering - Artificial Neural Networks
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Integrated Computer-Aided Engineering - Artificial Neural Networks
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Integrated Computer-Aided Engineering - Artificial Neural Networks
Integrated Computer-Aided Engineering - Artificial Neural Networks
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Integrated Computer-Aided Engineering - Artificial Neural Networks
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Integrated Computer-Aided Engineering - Artificial Neural Networks
Two-layer automatic sound classification system for conversation enhancement in hearing aids
Integrated Computer-Aided Engineering
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IEEE Transactions on Neural Networks
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
Supervised Learning Probabilistic Neural Networks
Neural Processing Letters
Expert Systems with Applications: An International Journal
Neural networks to predict earthquakes in Chile
Applied Soft Computing
Generalized classifier neural network
Neural Networks
Global structural optimization considering expected consequences of failure and using ANN surrogates
Computers and Structures
Pedestrian detection in far infrared images
Integrated Computer-Aided Engineering
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A probabilistic neural network (PNN) is presented for predicting the magnitude of the largest earthquake in a pre-defined future time period in a seismic region using eight mathematically computed parameters known as seismicity indicators. The indicators considered are the time elapsed during a particular number (n) of significant seismic events before the month in question, the slope of the Gutenberg-Richter inverse power law curve for the n events, the mean square deviation about the regression line based on the Gutenberg-Richter inverse power law for the n events, the average magnitude of the last n events, the difference between the observed maximum magnitude among the last n events and that expected through the Gutenberg-Richter relationship known as the magnitude deficit, the rate of square root of seismic energy released during the n events, the mean time or period between characteristic events, and the coefficient of variation of the mean time. Prediction accuracies of the model are evaluated using three different statistical measures: the probability of detection, the false alarm ratio, and the true skill score or R score. The PNN model is trained and tested using data for the Southern California region. The model yields good prediction accuracies for earthquakes of magnitude between 4.5 and 6.0. The PNN model presented in this paper complements the recurrent neural network model developed by the authors previously, where good results were reported for predicting earthquakes with magnitude greater than 6.0.