Download or read book Potential Applications of Neural Networks to Verification and Validation of Complex Systems written by Ö Ciftcioglu and published by . This book was released on 1992 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Methods and Procedures for the Verification and Validation of Artificial Neural Networks written by Brian J. Taylor and published by Springer Science & Business Media. This book was released on 2006-03-20 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Verification and Validation of Complex Systems Human Factors Issues written by John A. Wise and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Guidance for the Verification and Validation of Neural Networks written by Laura L. Pullum and published by John Wiley & Sons. This book was released on 2007-03-09 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Issues in Verification and Validation of Neural Network Based Approaches for Fault diagnosis in Autonomous Systems written by Uma Bharathi Ramachandran and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book RIACS Workshop on the Verification and Validation of Autonomous and Adaptive Systems written by Charles Pecheur and published by . This book was released on 2001 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Hybrid Architectures for Intelligent Systems written by Abraham Kandel and published by CRC Press. This book was released on 2020-09-10 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Verification and Validation of Neural Networks for Aerospace Systems written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-06-12 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Neural Network Verification for Nonlinear Systems written by Chelsea Rose Sidrane and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Leveraging Applications of Formal Methods Verification and Validation Adaptation and Learning written by Tiziana Margaria and published by Springer Nature. This book was released on 2022-10-19 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book ASME Technical Papers written by and published by . This book was released on with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings of the Estonian Academy of Sciences Engineering written by and published by . This book was released on 2005-03 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Neural Networks in High Assurance Systems written by Johann M.Ph. Schumann and published by Springer Science & Business Media. This book was released on 2010-02-28 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Applications of Neural Networks in High Assurance Systems" is the first book directly addressing a key part of neural network technology: methods used to pass the tough verification and validation (V&V) standards required in many safety-critical applications. The book presents what kinds of evaluation methods have been developed across many sectors, and how to pass the tests. A new adaptive structure of V&V is developed in this book, different from the simple six sigma methods usually used for large-scale systems and different from the theorem-based approach used for simplified component subsystems.
Download or read book Neural Networks and Their Applications written by John G. Taylor and published by John Wiley & Sons. This book was released on 1996 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are one of the fast-growing paradigms for learning systems with a wide variety of potential applications in industry. In particular there are general results which prove the universal applicability of neural networks to many problems. There is also an ever greater understanding of the underlying manner in which tasks such as classification can be solved optimally by this host of techniques. Through the application of ideas of statistics, dynamical systems theory and information theory the methods are likely to become ever more effective for solving problems previously found to be difficult to tackle using standard techniques. This book compares and contrasts the academic theory and the industrial reality, with case studies and latest research findings from international experts. The contributions describe application areas including finance, digital data transmission, hybrid systems, automotive and aerospace industries, pattern analysis in clinical psychiatry, time series prediction, and genetic and neural algorithms. This book demonstrates the vigour and strength of the subject in solving hard problems and as such will be of great interest to all researchers and professionals with an interest in neural networks.
Download or read book Verification and Validation of Neural Networks for Aerospace Systems written by and published by . This book was released on 2002 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Paper written by and published by . This book was released on 1990 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Systems Engineering Neural Networks written by Alessandro Migliaccio and published by John Wiley & Sons. This book was released on 2023-01-10 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: SYSTEMS ENGINEERING NEURAL NETWORKS A complete and authoritative discussion of systems engineering and neural networks In Systems Engineering Neural Networks, a team of distinguished researchers deliver a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems engineering mindset. In the book, you’ll find a general theoretical discussion of both systems engineering and neural networks accompanied by coverage of relevant and specific topics, from deep learning fundamentals to sport business applications. Readers will discover in-depth examples derived from many years of engineering experience, a comprehensive glossary with links to further reading, and supplementary online content. The authors have also included a variety of applications programmed in both Python 3 and Microsoft Excel. The book provides: A thorough introduction to neural networks, introduced as key element of complex systems Practical discussions of systems engineering and forecasting, complexity theory and optimization and how these techniques can be used to support applications outside of the traditional AI domains Comprehensive explorations of input and output, hidden layers, and bias in neural networks, as well as activation functions, cost functions, and back-propagation Guidelines for software development incorporating neural networks with a systems engineering methodology Perfect for students and professionals eager to incorporate machine learning techniques into their products and processes, Systems Engineering Neural Networks will also earn a place in the libraries of managers and researchers working in areas involving neural networks.
