£96.21

Springer - Applications of Neural Networks in High Assurance Systems

9478649

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Description

Bridging the gap between neural network technology and safety-critical requirements, this book by Springer provides essential guidance on verification and validation (V&V) standards. As AI becomes more common in sensitive sectors, the ability to pass rigorous testing is vital. This text is the first to address the specific methods needed to meet these high-level standards. Readers will explore various evaluation methods developed across multiple industries and learn practical ways to pass necessary tests. Unlike traditional approaches, this book introduces a new adaptive structure for V&V. This method moves beyond the simple six sigma techniques used for large-scale systems and offers an alternative to the theorem-based approach used for simplified component subsets. It is a specialized resource for those working within the Studies in Computational Intelligence series, focusing on making neural networks reliable for high-assurance environments.

Key Features

Addresses essential verification and validation (V&V) methods required for safety-critical neural network applications.

Provides a look at diverse evaluation methods developed across many different industrial sectors.

Introduces a new adaptive structure for V&V that differs from standard six sigma methods.

Offers an alternative to the theorem-based approach typically used for simplified component subsets.

Part of the Studies in Computational Intelligence series (Volume 268) from Springer.

Product Specifications

Model
9478649
Format
hardcover
Domain
Amazon UK
Release Date
28 February 2010
Listed Since
27 October 2009

Barcode

No barcode data available

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