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 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 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 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 Knowledge Based Aircraft Automation written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-07-08 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network dev...
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 Artificial Intelligence with Applications for Aircraft written by L. Harrison and published by . This book was released on 1994 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Digital Systems Validation Chapter 20 Artificial Intelligence with Applications for Aircraft Handbook written by and published by . This book was released on 1994 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This chapter provides an overview of Artificial Intelligence (Al) technology, one of the more complex applications of digital systems. This chapter examines A1-based technology, focusing on three fields: neural networks, fuzzy' logic, and Expert Systems. This chapter provides the reader with the background and a basic understanding of the fundamental at those fields. Another section examines aspects of the Al development environment, including languages, tools, and AI-based hardware components. Some of the proposed aviation-related applications for both civil and military aircraft, including pilot assistants and diagnostic aids, are surveyed. Additionally, certification issues, including regulations, guidelines, and verification and validation techniques are examined. Human factors issues relating to the use of this technology are identified and reviewed. In addition, this chapter identifies safety issues and concerns over the use of this technology in airborne systems.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Safe and Efficient Aircraft Guidance and Control Using Neural Networks written by Kyle David Julian and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous systems have the potential to reduce costs and increase safety for a variety of applications, including aviation. Whereas automation is used for problems with limited scope, autonomous systems must reason about complex scenarios, including low-probability safety-critical events, where the correct behavior cannot be enumerated. As a result, autonomous systems use computers to reason and make decisions. One method for computing decisions uses a form of optimization called dynamic programming, but the curse of dimensionality leads to large representations of the decision-making policy. One approach to reduce the representation size is to approximate the decision logic. This thesis presents a neural network compression method that trains an accurate neural network approximation of decision logic score tables. Rather than storing the score table in memory, only the neural network parameters need to be stored, reducing the representation size by a factor of 1000 or more. Experiments with Monte Carlo simulations and flight testing indicate that a neural network representation can perform as well as the original policy. Although simulations and flight testing can instill confidence, a finite number of simulations does not guarantee that the neural network behaves correctly in all possible scenarios, as neural networks are well known to behave in unexpected ways. Verifying that the neural network issues safe actions in all scenarios is necessary before they can be used in safety-critical systems. This thesis presents two methods that reason about the weights of the neural networks and the dynamics of the state variables describing the scenario to determine if the neural network makes safe decisions in all scenarios. The first method analyzes the dynamics to compute a region of state variables for each action where that action cannot be safely given. Then, analysis of the network weights determines if any input variables could result in the neural network giving an unsafe action. If these neural network properties are verified, then the neural network is guaranteed to behave safely in all scenarios. If the properties do not hold and the neural network gives an action in its unsafe region, the system as a whole is not necessarily unsafe. If prior neural network actions prevent the system from reaching states where unsafe actions are given, then the neural network may still be safe. This thesis presents a reachability method to determine if unsafe states can be reached using the neural network actions. Beginning with a set of initial states, the reachability method uses the neural network policy and system dynamics to compute the set of states that could be reached at the next time step. The analysis can be repeated to compute the set of states that can be reached over time, ending when the reachable set includes an unsafe state or converges to a steady-state safe set. The reachability method guarantees that the neural network behaves safely if no unsafe states are reachable from the initial set. The two methods described previously verify safety when using a neural network controller, but these methods do not scale well with the dimensionality of the state space. For neural networks with high-dimensional inputs, such as images, these verification methods are intractable. This thesis presents an approach to validate a neural network controller by searching for small input disturbances that cause the neural network controller to reach an unsafe state. The validation method combines reinforcement learning algorithms with analysis of the neural network weights to find the most likely sequence of input disturbances that causes the system to fail. The method scales well for image-based neural networks, and inspection of the failure sequence either reveals system weaknesses or validates that the system requires unrealistic disturbances to fail.
Download or read book Neural Information Processing written by Jun Wang and published by Springer. This book was released on 2006-10-03 with total page 1248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions.
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 Locally Linear Neural Networks for Aerospace Navigation Systems written by Steven C. Gustafson and published by . This book was released on 1991 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network software simulations for the representation and prediction of aircraft inertial navigation system (INS) data were developed. These simulations were evaluated using flight test data that sampled INS outputs at a standard rate for neural network testing and at half this rate for neural network training. The simulations used both locally linear neural networks and backpropagation-trained neural networks. Locally linear neural networks have several desirable properties for this application, including interpolation of the training data and representation of linear relationships. For the flight test data two milliradian testing accuracy was generally achieved with five successive and prior INS heading, pitch, and roll increments as inputs.
Download or read book Proceedings of the 28th Annual International Computer Software and Applications Conference written by and published by . This book was released on 2004 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptive Control Approach For Software Quality Improvement written by W Eric Wong and published by World Scientific. This book was released on 2011-06-30 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Diagnostics and Prognostics of Aerospace Engines written by Ravi Rajamani and published by SAE International. This book was released on 2018-11-28 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: The propulsion system is arguably the most critical part of the aircraft; it certainly is the single most expensive component of the vehicle. Ensuring that engines operate reliably without major maintenance issues is an important goal for all operators, military or commercial. Engine health management (EHM) is a critical piece of this puzzle and has been a part of the engine maintenance for more than five decades. In fact, systematic condition monitoring was introduced for engines before it was applied to other systems on the aircraft. Diagnostics and Prognostics of Aerospace Engines is a collection of technical papers from the archives of SAE International, which introduces the reader to a brief history of EHM, presents some examples of EHM functions, and outlines important future trends. The goal of engine health maintenance is ultimately to reduce the cost of operations by catching problems before they become major issues, by helping reduce repair times through diagnostics, and by facilitating logistic optimization through prognostic estimates. Diagnostics and Prognostics of Aerospace Engines shows that the essence of these goals has not changed over time.
