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Author: Ö Ciftcioglu
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
Author: Ö Ciftcioglu
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
Get Book Here
Book Description
Author: Brian J. Taylor
Publisher: Springer Science & Business Media
ISBN: 0387294856
Category : Computers
Languages : en
Pages : 280
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Book Description
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.
Author: John A. Wise
Publisher: Springer Science & Business Media
ISBN: 3662029332
Category : Computers
Languages : en
Pages : 682
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Book Description
Despite its increasing importance, the verification and validation of the human-machine interface is perhaps the most overlooked aspect of system development. Although much has been written about the design and developmentprocess, very little organized information is available on how to verifyand validate highly complex and highly coupled dynamic systems. Inability toevaluate such systems adequately may become the limiting factor in our ability to employ systems that our technology and knowledge allow us to design. This volume, based on a NATO Advanced Science Institute held in 1992, is designed to provide guidance for the verification and validation of all highly complex and coupled systems. Air traffic control isused an an example to ensure that the theory is described in terms that will allow its implementation, but the results can be applied to all complex and coupled systems. The volume presents the knowledge and theory ina format that will allow readers from a wide variety of backgrounds to apply it to the systems for which they are responsible. The emphasis is on domains where significant advances have been made in the methods of identifying potential problems and in new testing methods and tools. Also emphasized are techniques to identify the assumptions on which a system is built and to spot their weaknesses.
Author: Laura L. Pullum
Publisher: John Wiley & Sons
ISBN: 047008457X
Category : Computers
Languages : en
Pages : 146
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Book Description
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.
Author: Uma Bharathi Ramachandran
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Autonomous systems are those that evolve over time, and through learning, can make intelligent decisions when faced with unidentified and unknown situations. Artificial Neural Networks (ANN) has been applied to an increasing number of real-world problems with considerable complexity. Due to their learning abilities, ANN-based systems have been increasingly attracting attention in applications where autonomy is critical and where identification of possible fault scenarios is not exhaustive before hand. We have proposed a methodology in which the learning rules that a trained network has adapted can be extracted and refined using rule extraction and rule refinement techniques, respectively, and then these refined rules are subsequently formally specified and verified against requirements specification using formal methods. The effectiveness of the proposed approach has been demonstrated using a case study of an attitude control subsystem of a satellite.
Author: National Aeronautics and Space Administration (NASA)
Publisher: Createspace Independent Publishing Platform
ISBN: 9781721037605
Category :
Languages : en
Pages : 86
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Book Description
The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: 1) Overview of Adaptive Systems; and 2) V&V Processes/Methods.Mackall, Dale and Nelson, Stacy and Schumman, Johann and Clancy, Daniel (Technical Monitor)Ames Research Center; Armstrong Flight Research CenterAEROSPACE SYSTEMS; NEURAL NETS; SOFTWARE ENGINEERING; PROGRAM VERIFICATION (COMPUTERS); ADAPTIVE CONTROL; FLIGHT CONTROL; PERFORMANCE TESTS; COMPUTERIZED SIMULATION; SENSITIVITY ANALYSIS; AIRCRAFT STRUCTURES
Author: Charles Pecheur
Publisher:
ISBN:
Category : Computer software
Languages : en
Pages : 12
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Book Description
The long-term future of space exploration at NASA is dependent on the full exploitation of autonomous and adaptive systems : careful monitoring of missions from earth, as is the norm now, will be infeasible due to the sheer number of proposed missions and the communication lag for deep-space missions. Mission managers are however worried about the reliability of these more intelligent systems. The main focus of the workshop was to address these worries and hence we invited NASA engineers working on autonomous and adaptive systems and researchers interested in the verification and validation ( V & V ) of software systems. The dual purpose of the meeting was to (1) make NASA engineers aware of the V & V techniques they could be using and (2) make the V& V community aware of the complexity of the systems NASA is developing.
Author: Abraham Kandel
Publisher: CRC Press
ISBN: 1000141446
Category : Computers
Languages : en
Pages : 450
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Book Description
Hybrid architecture for intelligent systems is a new field of artificial intelligence concerned with the development of the next generation of intelligent systems. This volume is the first book to delineate current research interests in hybrid architectures for intelligent systems. The book is divided into two parts. The first part is devoted to the theory, methodologies, and algorithms of intelligent hybrid systems. The second part examines current applications of intelligent hybrid systems in areas such as data analysis, pattern classification and recognition, intelligent robot control, medical diagnosis, architecture, wastewater treatment, and flexible manufacturing systems. Hybrid Architectures for Intelligent Systems is an important reference for computer scientists and electrical engineers involved with artificial intelligence, neural networks, parallel processing, robotics, and systems architecture.
Author: Chelsea Rose Sidrane
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Machine learning has proven useful in a wide variety of domains from computer vision to control of autonomous systems. However, if we want to use neural networks in safety critical systems such as vehicles and aircraft, we need reliability guarantees. We turn to formal methods to verify that neural networks do not have unexpected behavior, such as misclassifying an image after a small amount of random noise is added. Within formal methods, there is a small but growing body of work focused on neural network verification. However, most of this work only reasons about neural networks in isolation, when in reality, neural networks are often used within large, complex systems. We build on this literature to verify neural networks operating within nonlinear systems. Our first contribution is to enable the use of mixed-integer linear programming for verification of systems containing both ReLU neural networks and smooth nonlinear functions. Mixed-integer linear programming is a common tool used for verifying neural networks with ReLU activation functions, and while effective, does not natively permit the use of nonlinear functions. We introduce an algorithm to overapproximate arbitrary nonlinear functions using piecewise linear constraints. These piecewise linear constraints can be encoded into a mixed-integer linear program, allowing verification of systems containing both ReLU neural networks and nonlinear functions. We use a special kind of approximation known as overapproximation which allows us to make sound claims about the original nonlinear system when we verify the overapproximate system. The next two contributions of this thesis are to apply the overapproximation algorithm to two different neural network verification settings: verifying inverse model neural networks and verifying neural network control policies. Frequently appearing in a variety of domains from medical imaging to state estimation, inverse problems involve reconstructing an underlying state from observations. The model mapping states to observations can be nonlinear and stochastic, making the inverse problem difficult. Neural networks are ideal candidates for solving inverse problems because they are very flexible and can be trained from data. However, inverse model neural networks lack built-in accuracy guarantees. We introduce a method to solve for verified upper bounds on the error of an inverse model neural network. The next verification setting we address is verifying neural network control policies for nonlinear dynamical systems. A control policy directs a dynamical system to perform a desired task such as moving to a target location. When a dynamical system is highly nonlinear and difficult to control, traditional control approaches may become computationally intractable. In contrast, neural network control policies are fast to execute. However, neural network control policies lack the stability, safety, and convergence guarantees that are often available to more traditional control approaches. In order to assess the safety and performance of neural network control policies, we introduce a method to perform finite time reachability analysis. Reachability analysis reasons about the set of states reachable by the dynamical system over time and whether that set of states is unsafe or is guaranteed to reach a goal. The final contribution of this thesis is the release of three open source software packages implementing methods described herein. The field of formal verification for neural networks is small and the release of open source software will allow it to grow more quickly as it makes iteration upon prior work easier. Overall, this thesis contributes ideas, methods, and tools to build confidence in deep learning systems. This area will continue to grow in importance as deep learning continues to find new applications.
Author: Tiziana Margaria
Publisher: Springer Nature
ISBN: 3031197593
Category : Computers
Languages : en
Pages : 483
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Book Description
This four-volume set LNCS 13701-13704 constitutes contributions of the associated events held at the 11th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2022, which took place in Rhodes, Greece, in October/November 2022. The contributions in the four-volume set are organized according to the following topical sections: specify this - bridging gaps between program specification paradigms; x-by-construction meets runtime verification; verification and validation of concurrent and distributed heterogeneous systems; programming - what is next: the role of documentation; automated software re-engineering; DIME day; rigorous engineering of collective adaptive systems; formal methods meet machine learning; digital twin engineering; digital thread in smart manufacturing; formal methods for distributed computing in future railway systems; industrial day.
