Development And Analysis Of Non Standard Echo State Networks
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Artificial Neural Networks - ICANN 2007
Author | : Joaquim Marques de Sá |
Publisher | : Springer |
Total Pages | : 999 |
Release | : 2007-09-14 |
Genre | : Computers |
ISBN | : 3540746900 |
This book is the first of a two-volume set that constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, September 2007. Coverage includes advances in neural network learning methods, advances in neural network architectures, neural dynamics and complex systems, data analysis, evolutionary computing, agents learning, as well as temporal synchronization and nonlinear dynamics in neural networks.
Advances in Data Analysis with Computational Intelligence Methods
Author | : Adam E Gawęda |
Publisher | : Springer |
Total Pages | : 417 |
Release | : 2017-09-21 |
Genre | : Technology & Engineering |
ISBN | : 3319679465 |
This book is a tribute to Professor Jacek Żurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Żurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few. At the same time, the book recognizes and honors Professor Żurada’s dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world’s largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields. The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.
Hydrological Processes Modelling and Data Analysis
Author | : Vijay P. Singh |
Publisher | : Springer Nature |
Total Pages | : 298 |
Release | : |
Genre | : |
ISBN | : 9819713161 |
Advances in Neural Networks -- ISNN 2011
Author | : Derong Liu |
Publisher | : Springer |
Total Pages | : 667 |
Release | : 2011-05-20 |
Genre | : Computers |
ISBN | : 3642211054 |
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.
Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022
Author | : Aboul Ella Hassanien |
Publisher | : Springer Nature |
Total Pages | : 844 |
Release | : 2022-11-17 |
Genre | : Technology & Engineering |
ISBN | : 3031206010 |
This proceedings book constitutes the refereed proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics (AISI 2021), which took place in Cairo, Egypt, during November 20–22, 2022, and is an international interdisciplinary conference that presents a spectrum of scientific research on all aspects of informatics and intelligent systems, technologies, and applications.
Nonlinear Modeling
Author | : Johan A. K. Suykens |
Publisher | : Springer Science & Business Media |
Total Pages | : 284 |
Release | : 1998-06-30 |
Genre | : Language Arts & Disciplines |
ISBN | : 9780792381952 |
This collection of eight contributions presents advanced black-box techniques for nonlinear modeling. The methods discussed include neural nets and related model structures for nonlinear system identification, enhanced multi-stream Kalman filter training for recurrent networks, the support vector method of function estimation, parametric density estimation for the classification of acoustic feature vectors in speech recognition, wavelet based modeling of nonlinear systems, nonlinear identification based on fuzzy models, statistical learning in control and matrix theory, and nonlinear time- series analysis. The volume concludes with the results of a time- series prediction competition held at a July 1998 workshop in Belgium. Annotation copyrighted by Book News, Inc., Portland, OR.
A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation
Author | : Hongjun Guan |
Publisher | : Infinite Study |
Total Pages | : 18 |
Release | : |
Genre | : Mathematics |
ISBN | : |
In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data.
Recurrent Neural Networks for Short-Term Load Forecasting
Author | : Filippo Maria Bianchi |
Publisher | : Springer |
Total Pages | : 74 |
Release | : 2017-11-09 |
Genre | : Computers |
ISBN | : 3319703382 |
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Optical Communication Systems
Author | : Andrew Ellis |
Publisher | : CRC Press |
Total Pages | : 285 |
Release | : 2019-09-02 |
Genre | : Computers |
ISBN | : 042964826X |
Telecommunications have underpinned social interaction and economic activity since the 19th century and have been increasingly reliant on optical fibers since their initial commercial deployment by BT in 1983. Today, mobile phone networks, data centers, and broadband services that facilitate our entertainment, commerce, and increasingly health provision are built on hidden optical fiber networks. However, recently it emerged that the fiber network is beginning to fill up, leading to the talk of a capacity crunch where the capacity still grows but struggles to keep up with the increasing demand. This book, featuring contributions by the suppliers of widely deployed simulation software and academic authors, illustrates the origins of the limited performance of an optical fiber from the engineering, physics, and information theoretic viewpoints. Solutions are then discussed by pioneers in each of the respective fields, with near-term solutions discussed by industrially based authors, and more speculative high-potential solutions discussed by leading academic groups.