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Sensitivity Analysis for Neural Networks

Product Image: Sensitivity Analysis for Neural Networks

Sensitivity Analysis for Neural Networks

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Description
Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters.
This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
Product details
Edition:
1
Number of Pages:
96
Release Date:
2009-11-18
Publication Date:
2009-11-18
Publisher:
Springer
Languages:
Original: English
ISBN10:
3642025315
ISBN13:
9783642025310
GPSR Manufacturer Reference:
Weight:
319 g
Height:
160 cm
Width:
241 cm
Thickness:
9 cm

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The items bear minimal signs of past use, such as light scratches or memories in the form of markings. These signs of wear give the items a charming character and tell stories of their previous owners, while not affecting their functionality.
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