Accelerating neural network training using weight extrapolations
Neural Networks
Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications
Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications
Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach
Computers & Geosciences
Multistrategy self-organizing map learning for classification problems
Computational Intelligence and Neuroscience
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The present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Network models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with a learning rate of 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found to be skillful. But the Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.