In response to an increasing demand for novel computing methods, Neural Networks for Applied Sciences and Engineering provides a simple but systematic introduction to neural networks applications. This book features case studies that use real data to demonstrate practical applications. It contains in-depth discussions of data, model validation, and uncertainty assessment issues, as well as data dimensionality and methods to reduce dimensionality. It provides detailed coverage of neural network types for extracting nonlinear ...
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In response to an increasing demand for novel computing methods, Neural Networks for Applied Sciences and Engineering provides a simple but systematic introduction to neural networks applications. This book features case studies that use real data to demonstrate practical applications. It contains in-depth discussions of data, model validation, and uncertainty assessment issues, as well as data dimensionality and methods to reduce dimensionality. It provides detailed coverage of neural network types for extracting nonlinear patterns in multi-dimension scientific data, including multi-layer perceptron for nonlinear prediction and classification, self organization maps for discovery of unknown data clusters and recurrent networks for nonlinear time-series forecasting.
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