What would animals say if we asked the right questions? / / Vinciane Despret ; translated by Brett Buchanan ; foreword by Bruno Latour

• Record Nr.: UNINA9910809629803321
• Autore: Despret Vinciane
• Pubbl/distr/stampa: Minneapolis, Minnesota ; ; London, [England] : , : University of Minnesota Press, , 2016; ©2016
• ISBN: 1-4529-5053-9
• Descrizione fisica: 1 online resource (276 p.)
• Collana: Posthumanities ; ; 38
• Disciplina: 591.5
• Soggetti: Animal behavior
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: Cover; Half Title; Title; Copyright; Contents; Foreword: The Scientific Fables of an Empirical La Fontaine Bruno Latour; Acknowledgments; How to Use This Book; Translator's Note; A for Artists: Stupid like a painter?; B for Beasts: Do apes really ape?; C for Corporeal: Is it all right to urinate in front of animals?; D for Delinquents: Can animals revolt?; E for Exhibitionists: Do animals see themselves as we see them?; F for Fabricating Science: Do animals have a sense of prestige?; G for Genius: With whom would extraterrestrials want to negotiate?; H for Hierarchies: Might the dominance of males be a myth?I for Impaired: Are animals reliable models of morality?; J for Justice: Can animals compromise?; K for Killable: Are any species killable?; L for Laboratory: What are rats interested in during experiments?; M for Magpies: How can we interest elephants in mirrors?; N for Necessity: Can one lead a rat to infanticide?; O for Oeuvres: Do birds make art?; P for Pretenders: Can deception be proof of good manners?; Q for Queer: Are penguins coming out of the closet?; R for Reaction: Do goats agree with statistics?; S for Separations: Can animals be broken down?T for Tying Knots: Who invented language and mathematics?; U for Umwelt: Do beasts know ways of being in the world?; V for Versions: Do chimpanzees die like we do?; W for Work: Why do we say that cows don't do anything?; X for Xenografts: Can one live with the heart of a pig?; Y for YouTube: Are ; animals the new celebrities?; Z for Zoophilia: Can horses consent?; Notes; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; R; S; T; V; W; X; Y; Z

Kalman filtering and neural networks / / edited by Simon Haykin

• Record Nr.: UNINA9911019785303321
• Pubbl/distr/stampa: New York, : Wiley, c2001
• ISBN: 9786610367566; 9781280367564; 1280367563; 9780470312261; 0470312262; 9780471464211; 047146421X; 9780471221548; 0471221546
• Descrizione fisica: 1 online resource (302 p.)
• Collana: Adaptive and learning systems for signal processing, communications, and control
• Altri autori (Persone): HaykinSimon S. <1931->
• Disciplina: 006.3/2; 621.3815324
• Soggetti: Kalman filtering; Neural networks (Computer science)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: KALMAN FILTERING AND NEURAL NETWORKS; CONTENTS; Preface; Contributors; 1 Kalman Filters; 1.1 Introduction; 1.2 Optimum Estimates; 1.3 Kalman Filter; 1.4 Divergence Phenomenon: Square-Root Filtering; 1.5 Rauch-Tung-Striebel Smoother; 1.6 Extended Kalman Filter; 1.7 Summary; References; 2 Parameter-Based Kalman Filter Training: Theory and Implementation; 2.1 Introduction; 2.2 Network Architectures; 2.3 The EKF Procedure; 2.3.1 Global EKF Training; 2.3.2 Learning Rate and Scaled Cost Function; 2.3.3 Parameter Settings; 2.4 ; Decoupled EKF (DEKF); 2.5 Multistream Training; 2.5.1 Some Insight into the Multistream Technique2.5.2 Advantages and Extensions of Multistream Training; 2.6 Computational Considerations; 2.6.1 Derivative Calculations; 2.6.2 Computationally Efficient Formulations for Multiple-Output Problems; 2.6.3 Avoiding Matrix Inversions; 2.6.4 Square-Root Filtering; 2.7 Other Extensions and Enhancements; 2.7.1 EKF Training with Constrained Weights; 2.7.2 EKF Training with an Entropic Cost Function; 2.7.3 EKF Training with Scalar Errors; 2.8 Automotive Applications of EKF Training; 2.8.1 Air/Fuel Ratio Control; 2.8.2 Idle Speed Control; 2.8.3 Sensor-Catalyst Modeling2.8.4 Engine Misfire Detection; 2.8.5 Vehicle Emissions Estimation; 2.9 Discussion; 2.9.1 Virtues of EKF Training; 2.9.2 Limitations of EKF Training; 2.9.3 Guidelines for Implementation and Use; References; 3 Learning Shape and Motion from Image Sequences; 3.1 Introduction; 3.2 Neurobiological and Perceptual Foundations of our Model; 3.3 Network Description; 3.4 Experiment 1; 3.5 Experiment 2; 3.6 Experiment 3; 3.7 Discussion; References; 4 Chaotic Dynamics; 4.1 Introduction; 4.2 Chaotic (Dynamic) Invariants; 4.3 Dynamic Reconstruction; 4.4 Modeling Numerically Generated Chaotic Time Series4.4.1 Logistic Map; 4.4.2 Ikeda Map; 4.4.3 Lorenz Attractor; 4.5 Nonlinear Dynamic Modeling of Real-World Time Series; 4.5.1 Laser Intensity Pulsations; 4.5.2 Sea Clutter Data; 4.6 Discussion; References; 5 Dual Extended Kalman Filter Methods; 5.1 Introduction; 5.2 Dual EKF-Prediction Error; 5.2.1 EKF-State Estimation; 5.2.2 EKF-Weight Estimation; 5.2.3 Dual Estimation; 5.3 A Probabilistic Perspective; 5.3.1 Joint Estimation Methods; 5.3.2 Marginal Estimation Methods; 5.3.3 Dual EKF Algorithms; 5.3.4 Joint EKF; 5.4 Dual EKF Variance Estimation5.5 Applications; 5.5.1 Noisy Time-Series Estimation and Prediction; 5.5.2 Economic Forecasting-Index of Industrial Production; 5.5.3 Speech Enhancement; 5.6 Conclusions; Acknowledgments; Appendix A: Recurrent Derivative of the Kalman Gain; Appendix B: Dual EKF with Colored Measurement Noise; References; 6 Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm; 6.1 Learning Stochastic Nonlinear Dynamics; 6.1.1 State Inference and Model Learning; 6.1.2 The Kalman Filter; 6.1.3 The EM Algorithm; 6.2 Combining EKS and EM; 6.2.1 Extended Kalman Smoothing (E-step)
• Sommario/riassunto: State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. O