Weighted Nonlinear Line Attractor for Complex Manifold Learning
An artificial neural network is modeled by weighting between different neurons to form synaptic connections. The nonlinear line attractor (NLA) models the weighting architecture by a polynomial weight set, which provides stronger connections between neuro
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Juan Julián Merelo Agostinho Rosa José M. Cadenas António Dourado Correia Kurosh Madani António Ruano Joaquim Filipe Editors
Computational Intelligence International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12–14, 2015, Revised Selected Papers
Studies in Computational Intelligence Volume 669
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
About this Series The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/7092
Juan Julián Merelo Agostinho Rosa José M. Cadenas António Dourado Correia Kurosh Madani António Ruano Joaquim Filipe •
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Editors
Computational Intelligence International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12–14, 2015, Revised Selected Papers
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Editors Juan Julián Merelo Computer Architecture and Computer Technology Universidad de Granada Granada Spain
Kurosh Madani Images, Signals and Intelligence Systems Laboratory University PARIS-EST Creteil (UPEC) Créteil France
Agostinho Rosa aSEEB-ISR-IST Technical University of Lisbon (IST) Lisbon Portugal
António Ruano Campus de Gambelas Universidade do Algarve Faro Portugal
José M. Cadenas Facultad de Informática University of Murcia Murcia Spain
Joaquim Filipe Escola Superior de Tecnologia de Setúbal Polytechnic Institute of Setúbal/INSTICC Setúbal Portugal
António Dourado Correia Departamento de Engenharia Informatica University of Coimbra Coimbra Portugal
ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-319-48504-1 ISBN 978-3-319-48506-5 (eBook) DOI 10.1007/978-3-319-48506-5 Library of Congress Control Number: 2016957642 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or di
