Computer Intensive Methods In Control And Signal Processing
Download Computer Intensive Methods In Control And Signal Processing full books in PDF, epub, and Kindle. Read online free Computer Intensive Methods In Control And Signal Processing ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Kevin Warwick |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 312 |
| Release | : 2012-12-06 |
| Genre | : Technology & Engineering |
| ISBN | : 1461219965 |
Due to the rapid increase in readily available computing power, a corre sponding increase in the complexity of problems being tackled has occurred in the field of systems as a whole. A plethora of new methods which can be used on the problems has also arisen with a constant desire to deal with more and more difficult applications. Unfortunately by increasing the ac curacy in models employed along with the use of appropriate algorithms with related features, the resultant necessary computations can often be of very high dimension. This brings with it a whole new breed of problem which has come to be known as "The Curse of Dimensionality" . The expression "Curse of Dimensionality" can be in fact traced back to Richard Bellman in the 1960's. However, it is only in the last few years that it has taken on a widespread practical significance although the term di mensionality does not have a unique precise meaning and is being used in a slightly different way in the context of algorithmic and stochastic complex ity theory or in every day engineering. In principle the dimensionality of a problem depends on three factors: on the engineering system (subject), on the concrete task to be solved and on the available resources. A system is of high dimension if it contains a lot of elements/variables and/or the rela tionship/connection between the elements/variables is complicated.
| Author | : Václav Šmídl |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 241 |
| Release | : 2006-03-30 |
| Genre | : Technology & Engineering |
| ISBN | : 3540288201 |
Treating VB approximation in signal processing, this monograph is for academic and industrial research groups in signal processing, data analysis, machine learning and identification. It reviews distributional approximation, showing that tractable algorithms for parametric model identification can be generated in off-line and on-line contexts.
| Author | : Wolfgang Förstner |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 248 |
| Release | : 1997-05-01 |
| Genre | : Computers |
| ISBN | : 9783764357580 |
Acquiring spatial data for geoinformation systems is still mainly done by human operators who analyze images using classical photogrammetric equipment or digitize maps, possibly assisted by some low level image processing. Automation of these tasks is difficult due to the complexity of the object, the topography, and the deficiency of current pattern recognition and image analysis tools for achieving a reliable transition from the data to the high level description of topographic objects. It appears that progress in automation only can be achieved by incorporating domain-specific semantic models into the analysis procedures. This volume collects papers which were presented at the Workshop "SMATI '97". The workshop focused on "Semantic Modeling for the Acquisition of Topographic Information from Images and Maps." This volume offers a comprehensive selection of high-quality and in-depth contributions by experts of the field coming from leading research institutes, treating both theoretical and implementation issues and integrating aspects of photogrammetry, cartography, computer vision, and image understanding.
| Author | : Riccardo Zoppoli |
| Publisher | : Springer Nature |
| Total Pages | : 532 |
| Release | : 2019-12-17 |
| Genre | : Technology & Engineering |
| ISBN | : 3030296938 |
Neural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc. Features of the text include: • a general functional optimization framework; • thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; • comparison of classical and neural-network based methods of approximate solution; • bounds to the errors of approximate solutions; • solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several; • applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and • numerous, numerically detailed examples. The authors’ diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.
| Author | : Mirek Karny |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 323 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1447115236 |
In almost all areas of science and engineering, the use of computers and microcomputers has, in recent years, transformed entire subject areas. What was not even considered possible a decade or two ago is now not only possible but is also part of everyday practice. As a result, a new approach usually needs to be taken (in order) to get the best out of a situation. What is required is now a computer's eye view of the world. However, all is not rosy in this new world. Humans tend to think in two or three dimensions at most, whereas computers can, without complaint, work in n dimensions, where n, in practice, gets bigger and bigger each year. As a result of this, more complex problem solutions are being attempted, whether or not the problems themselves are inherently complex. If information is available, it might as well be used, but what can be done with it? Straightforward, traditional computational solutions to this new problem of complexity can, and usually do, produce very unsatisfactory, unreliable and even unworkable results. Recently however, artificial neural networks, which have been found to be very versatile and powerful when dealing with difficulties such as nonlinearities, multivariate systems and high data content, have shown their strengths in general in dealing with complex problems. This volume brings together a collection of top researchers from around the world, in the field of artificial neural networks.
| Author | : Olga Kosheleva |
| Publisher | : Springer Nature |
| Total Pages | : 638 |
| Release | : 2020-02-28 |
| Genre | : Computers |
| ISBN | : 3030310418 |
Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.
| Author | : George William Irwin |
| Publisher | : IET |
| Total Pages | : 320 |
| Release | : 1995 |
| Genre | : Computers |
| ISBN | : 9780852968529 |
The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and the fundamental control principles, these are followed by a series of application studies, most of which are industrially based, and the book concludes with a consideration of some recent research.
| Author | : Kenneth J. Hunt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 285 |
| Release | : 2012-12-06 |
| Genre | : Technology & Engineering |
| ISBN | : 1447130669 |
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, .... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Within the control community there has been much discussion of and interest in the new Emerging Technologies and Methods. Neural networks along with Fuzzy Logic and Expert Systems is an emerging methodology which has the potential to contribute to the development of intelligent control technologies. This volume of some thirteen chapters edited by Kenneth Hunt, George Irwin and Kevin Warwick makes a useful contribution to the literature of neural network methods and applications. The chapters are arranged systematically progressing from theoretical foundations, through the training aspects of neural nets and concluding with four chapters of applications. The applications include problems as diverse as oven tempera ture control, and energy/load forecasting routines. We hope this interesting but balanced mix of material appeals to a wide range of readers from the theoretician to the industrial applications engineer.
| Author | : R Murray-Smith |
| Publisher | : CRC Press |
| Total Pages | : 360 |
| Release | : 2020-11-26 |
| Genre | : Technology & Engineering |
| ISBN | : 1000162761 |
This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions. Many component methods are described, as well as discussion of the strategies available for building a successful multiple model approach.
| Author | : Tatiana V Guy |
| Publisher | : Springer |
| Total Pages | : 197 |
| Release | : 2013-02-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3642364063 |
Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process. The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider: · how a crowd of imperfect decision makers outperforms experts' decisions; · how to decrease decision makers' imperfection by reducing knowledge available; · how to decrease imperfection via automated elicitation of DM preferences; · a human's limited willingness to master the available decision-support tools as an additional source of imperfection; · how the decision maker's emotional state influences the rationality; a DM support of edutainment robot based on its system of values and respecting emotions. The book will appeal to anyone interested in the challenging topic of DM theory and its applications.
