Dynamic Properties of Neural Networks
Author : Dawei Dong
Publisher :
Page : 216 pages
File Size : 31,52 MB
Release : 1991
Category : Electronic dissertations
ISBN :
Author : Dawei Dong
Publisher :
Page : 216 pages
File Size : 31,52 MB
Release : 1991
Category : Electronic dissertations
ISBN :
Author : Andrea Crisanti
Publisher :
Page : 232 pages
File Size : 34,95 MB
Release : 1988
Category :
ISBN :
Author : Markos THANOS
Publisher :
Page : 0 pages
File Size : 22,3 MB
Release : 1969
Category :
ISBN :
Author : Avner Priel
Publisher :
Page : 358 pages
File Size : 15,62 MB
Release : 1999
Category : Dynamic programming
ISBN :
Author : Lingfei Wu
Publisher : Springer Nature
Page : 701 pages
File Size : 21,8 MB
Release : 2022-01-03
Category : Computers
ISBN : 9811660549
Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Author : Michel J.A.M. van Putten
Publisher : Springer Nature
Page : 259 pages
File Size : 40,73 MB
Release : 2020-12-18
Category : Science
ISBN : 3662611848
This book treats essentials from neurophysiology (Hodgkin–Huxley equations, synaptic transmission, prototype networks of neurons) and related mathematical concepts (dimensionality reductions, equilibria, bifurcations, limit cycles and phase plane analysis). This is subsequently applied in a clinical context, focusing on EEG generation, ischaemia, epilepsy and neurostimulation. The book is based on a graduate course taught by clinicians and mathematicians at the Institute of Technical Medicine at the University of Twente. Throughout the text, the author presents examples of neurological disorders in relation to applied mathematics to assist in disclosing various fundamental properties of the clinical reality at hand. Exercises are provided at the end of each chapter; answers are included. Basic knowledge of calculus, linear algebra, differential equations and familiarity with MATLAB or Python is assumed. Also, students should have some understanding of essentials of (clinical) neurophysiology, although most concepts are summarized in the first chapters. The audience includes advanced undergraduate or graduate students in Biomedical Engineering, Technical Medicine and Biology. Applied mathematicians may find pleasure in learning about the neurophysiology and clinic essentials applications. In addition, clinicians with an interest in dynamics of neural networks may find this book useful, too.
Author : Yuri Tiumentsev
Publisher : Academic Press
Page : 332 pages
File Size : 26,41 MB
Release : 2019-05-17
Category : Science
ISBN : 0128154306
Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area
Author : Nathaniel James-Michael Rodriguez
Publisher :
Page : 0 pages
File Size : 32,59 MB
Release : 2021
Category : Neural networks (Computer science)
ISBN :
Understanding the inner workings of neural networks is a paramount scientific challenge. The challenge is rooted in our pursuit to unravel the human mind and reproduce its intelligence in our own machines. This task transcends any single discipline and borrows knowledge and expertise from the brain sciences, physics, mathematics, network science, machine learning, and complex systems. Neural networks, both biological and artificial, are built from the same underlying principles. They are a system of non-linear elements connected together via a network through which they communicate to perform complex computations beyond the capacity of any single element. Neural networks display fantastically rich dynamical properties and computational abilities. It is crucial to understand how the structural organization of the network affects its dynamics and functional capabilities. In this thesis, I explore modularity, a prominent topological feature found in many brain networks. It holds a crucial key for unraveling the mystery of neural systems.
Author : Madan Gupta
Publisher : John Wiley & Sons
Page : 752 pages
File Size : 49,89 MB
Release : 2004-04-05
Category : Computers
ISBN : 0471460923
Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.
Author : S. A. Billings
Publisher :
Page : pages
File Size : 22,47 MB
Release : 1991
Category : Automatic control
ISBN :