Methods And Procedures For The Verification And Validation Of Artificial Neural Networks

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Methods and Procedures for the Verification and Validation of Artificial Neural Networks

Brian J. Taylor
Methods and Procedures for the Verification and Validation of Artificial Neural Networks Book PDF

Author : Brian J. Taylor
Publisher : Springer Science & Business Media
Page : 280 pages
File Size : 42,8 Mb
Release : 2006-03-20
Category : Computers
ISBN : 9780387294858

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Methods and Procedures for the Verification and Validation of Artificial Neural Networks by Brian J. Taylor Pdf

Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.

Guidance for the Verification and Validation of Neural Networks

Laura L. Pullum,Brian J. Taylor,Marjorie A. Darrah
Guidance for the Verification and Validation of Neural Networks Book PDF

Author : Laura L. Pullum,Brian J. Taylor,Marjorie A. Darrah
Publisher : John Wiley & Sons
Page : 146 pages
File Size : 47,6 Mb
Release : 2007-03-09
Category : Computers
ISBN : 9780470084571

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Guidance for the Verification and Validation of Neural Networks by Laura L. Pullum,Brian J. Taylor,Marjorie A. Darrah Pdf

This book provides guidance on the verification and validation of neural networks/adaptive systems. Considering every process, activity, and task in the lifecycle, it supplies methods and techniques that will help the developer or V&V practitioner be confident that they are supplying an adaptive/neural network system that will perform as intended. Additionally, it is structured to be used as a cross-reference to the IEEE 1012 standard.

Computational Intelligence in Automotive Applications

Danil Prokhorov
Computational Intelligence in Automotive Applications Book PDF

Author : Danil Prokhorov
Publisher : Springer Science & Business Media
Page : 374 pages
File Size : 43,8 Mb
Release : 2008
Category : Computers
ISBN : 9783540792567

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Computational Intelligence in Automotive Applications by Danil Prokhorov Pdf

This edited volume is the first of its kind and provides a representative sample of contemporary computational intelligence (CI) activities in the area of automotive technology. All chapters contain overviews of the state-of-the-art.

ADAS and Automated Driving

Plato Pathrose
ADAS and Automated Driving Book PDF

Author : Plato Pathrose
Publisher : SAE International
Page : 381 pages
File Size : 42,7 Mb
Release : 2024-03-01
Category : Transportation
ISBN : 9781468607451

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ADAS and Automated Driving by Plato Pathrose Pdf

"Immerse yourself in the evolving world of automotive technology with ADAS and Automated Driving - Systems Engineering. Explore advanced driver assistance systems (ADAS) and automated driving, revealing the automotive industry’s technological revolution. As technology becomes a driving force, this book serves as a guide to understanding cutting-edge technologies deployed by leading vehicle manufacturers. Discover how multiple systems synergize to provide ADAS and automated driving functions. Authored by an industry expert, this book explores systems engineering’s crucial role in designing, safety-critical cyber-physical systems. Gain practical insights into the processes and methods adapted for the current technological era of software-defined vehicles, influenced by AI, digitalization, and rapid technological advances. Whether you're a seasoned engineer navigating the shift to software-defined vehicles or a student eager to grasp systems engineering methods, this book is your key to unlocking the skills demanded in the exciting era of digitalization. Immerse yourself in real-world examples drawn from industry experiences, bridging the gap between theory and practical application. Gain the knowledge and expertise needed to embark on projects involving the intricate world of cyber-physical systems with ADAS and Automated Driving - Systems Engineering. “As this book demonstrates, systems engineering is needed more than ever to navigate the complexities of the type of projects where alternative delivery models are applied and to help ensure effective delivery even within the constraints of aggressive and adaptable schedules.” Dr David Ward Global Head of Vehicle Resilience—Functional Safety HORIBA MIRA Limited “This book holistically explains the lifecycle and the processes for ADAS and autonomous systems and their influence on the overall vehicle over its complete lifecycle.” Matthias Schulze Vice President, ADAS Product, ecarx" (ISBN 9781468607444, ISBN 9781468607451, ISBN 9781468607468, DOI 10.4271/9781468607451)

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
Deep Learning Techniques and Optimization Strategies in Big Data Analytics Book PDF

Author : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
Publisher : IGI Global
Page : 355 pages
File Size : 40,9 Mb
Release : 2019-11-29
Category : Computers
ISBN : 9781799811947

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Deep Learning Techniques and Optimization Strategies in Big Data Analytics by Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian Pdf

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Autonomy Requirements Engineering for Space Missions

Emil Vassev,Mike Hinchey
Autonomy Requirements Engineering for Space Missions Book PDF

Author : Emil Vassev,Mike Hinchey
Publisher : Springer
Page : 260 pages
File Size : 53,9 Mb
Release : 2014-08-27
Category : Computers
ISBN : 9783319098166

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Autonomy Requirements Engineering for Space Missions by Emil Vassev,Mike Hinchey Pdf

Advanced space exploration is performed by unmanned missions with integrated autonomy in both flight and ground systems. Risk and feasibility are major factors supporting the use of unmanned craft and the use of automation and robotic technologies where possible. Autonomy in space helps to increase the amount of science data returned from missions, perform new science, and reduce mission costs. Elicitation and expression of autonomy requirements is one of the most significant challenges the autonomous spacecraft engineers need to overcome today. This book discusses the Autonomy Requirements Engineering (ARE) approach, intended to help software engineers properly elicit, express, verify, and validate autonomy requirements. Moreover, a comprehensive state-of-the-art of software engineering for aerospace is presented to outline the problems handled by ARE along with a proof-of-concept case study on the ESA's BepiColombo Mission demonstrating the ARE’s ability to handle autonomy requirements.

Deep Learning for Autonomous Vehicle Control

Sampo Kuutti,Saber Fallah,Richard Bowden,Phil Barber
Deep Learning for Autonomous Vehicle Control Book PDF

Author : Sampo Kuutti,Saber Fallah,Richard Bowden,Phil Barber
Publisher : Springer Nature
Page : 70 pages
File Size : 53,8 Mb
Release : 2022-06-01
Category : Technology & Engineering
ISBN : 9783031015021

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Deep Learning for Autonomous Vehicle Control by Sampo Kuutti,Saber Fallah,Richard Bowden,Phil Barber Pdf

The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.

Artificial Neural Networks in Pattern Recognition

Friedhelm Schwenker,Neamat El Gayar
Artificial Neural Networks in Pattern Recognition Book PDF

Author : Friedhelm Schwenker,Neamat El Gayar
Publisher : Springer
Page : 280 pages
File Size : 50,7 Mb
Release : 2010-04-16
Category : Computers
ISBN : 9783642121593

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Artificial Neural Networks in Pattern Recognition by Friedhelm Schwenker,Neamat El Gayar Pdf

Artificial Neural Networks in Pattern Recognition synthesizes the proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010. Topics include supervised and unsupervised learning, feature selection, pattern recognition in signal and image processing.

An Introduction to Artificial Psychology

Hojjatollah Farahani,Marija Blagojević,Parviz Azadfallah,Peter Watson,Forough Esrafilian,Sara Saljoughi
An Introduction to Artificial Psychology Book PDF

Author : Hojjatollah Farahani,Marija Blagojević,Parviz Azadfallah,Peter Watson,Forough Esrafilian,Sara Saljoughi
Publisher : Springer Nature
Page : 262 pages
File Size : 46,6 Mb
Release : 2023-05-18
Category : Psychology
ISBN : 9783031311727

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An Introduction to Artificial Psychology by Hojjatollah Farahani,Marija Blagojević,Parviz Azadfallah,Peter Watson,Forough Esrafilian,Sara Saljoughi Pdf

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software.

Adaptive Control Approach for Software Quality Improvement

W. Eric Wong,Bojan Cukic
Adaptive Control Approach for Software Quality Improvement Book PDF

Author : W. Eric Wong,Bojan Cukic
Publisher : World Scientific
Page : 308 pages
File Size : 44,9 Mb
Release : 2011
Category : Computers
ISBN : 9789814340922

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Adaptive Control Approach for Software Quality Improvement by W. Eric Wong,Bojan Cukic Pdf

This book focuses on the topic of improving software quality using adaptive control approaches. As software systems grow in complexity, some of the central challenges include their ability to self-manage and adapt at run time, responding to changing user needs and environments, faults, and vulnerabilities. Control theory approaches presented in the book provide some of the answers to these challenges. The book weaves together diverse research topics (such as requirements engineering, software development processes, pervasive and autonomic computing, service-oriented architectures, on-line adaptation of software behavior, testing and QoS control) into a coherent whole. Written by world-renowned experts, this book is truly a noteworthy and authoritative reference for students, researchers and practitioners to better understand how the adaptive control approach can be applied to improve the quality of software systems. Book chapters also outline future theoretical and experimental challenges for researchers in this area.

Trends in Neural Computation

Ke Chen,Lipo Wang
Trends in Neural Computation Book PDF

Author : Ke Chen,Lipo Wang
Publisher : Springer
Page : 512 pages
File Size : 49,8 Mb
Release : 2006-11-15
Category : Computers
ISBN : 9783540361220

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Trends in Neural Computation by Ke Chen,Lipo Wang Pdf

Trends in Neural Computation includes twenty chapters contributed by leading experts or formed by extending well-selected papers presented in the 2005 International Conference on Natural Computation. The book reviews the latest progress in a range of different areas of neural computation, including theoretical neural computation, biologically plausible neural modeling, computational cognitive science, artificial neural networks – architectures and learning algorithms and their applications in real-world problems.

Artificial Neural Networks

Kevin L. Priddy,Paul E. Keller
Artificial Neural Networks Book PDF

Author : Kevin L. Priddy,Paul E. Keller
Publisher : SPIE Press
Page : 184 pages
File Size : 55,9 Mb
Release : 2005
Category : Computers
ISBN : 0819459879

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Artificial Neural Networks by Kevin L. Priddy,Paul E. Keller Pdf

This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.

Introduction to Neural Network Verification

Aws Albarghouthi
Introduction to Neural Network Verification Book PDF

Author : Aws Albarghouthi
Publisher : Unknown
Page : 182 pages
File Size : 49,7 Mb
Release : 2021-12-02
Category : Electronic
ISBN : 1680839101

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Introduction to Neural Network Verification by Aws Albarghouthi Pdf

Over the past decade, a number of hardware and software advances have conspired to thrust deep learning and neural networks to the forefront of computing. Deep learning has created a qualitative shift in our conception of what software is and what it can do: Every day we're seeing new applications of deep learning, from healthcare to art, and it feels like we're only scratching the surface of a universe of new possibilities. This book offers the first introduction of foundational ideas from automated verification as applied to deep neural networks and deep learning. It is divided into three parts: Part 1 defines neural networks as data-flow graphs of operators over real-valued inputs. Part 2 discusses constraint-based techniques for verification. Part 3 discusses abstraction-based techniques for verification. The book is a self-contained treatment of a topic that sits at the intersection of machine learning and formal verification. It can serve as an introduction to the field for first-year graduate students or senior undergraduates, even if they have not been exposed to deep learning or verification.

Deep Neural Networks and Data for Automated Driving

Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben
Deep Neural Networks and Data for Automated Driving Book PDF

Author : Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben
Publisher : Springer Nature
Page : 435 pages
File Size : 42,6 Mb
Release : 2022-07-19
Category : Technology & Engineering
ISBN : 9783031012334

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Deep Neural Networks and Data for Automated Driving by Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Pdf

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Artificial Neural Network Modelling

Subana Shanmuganathan,Sandhya Samarasinghe
Artificial Neural Network Modelling Book PDF

Author : Subana Shanmuganathan,Sandhya Samarasinghe
Publisher : Springer
Page : 472 pages
File Size : 55,5 Mb
Release : 2016-02-03
Category : Technology & Engineering
ISBN : 9783319284958

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Artificial Neural Network Modelling by Subana Shanmuganathan,Sandhya Samarasinghe Pdf

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.