Bio-Inspired Systems: Computational and Ambient Intelligence

1. Front Matter

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2. Theoretical Foundations and Models

   1. Lower Bounds for Approximation of Some Classes of Lebesgue Measurable Functions by Sigmoidal Neural Networks

      • José L. Montaña, Cruz E. Borges

      Pages 1-8

   2. A Wavelet Based Method for Detecting Multiple Encoding Rhythms in Neural Networks

      • Carlos Aguirre, Pedro Pascual

      Pages 9-16

   3. Switching Dynamics of Neural Systems in the Presence of Multiplicative Colored Noise

      • Jorge F. Mejias, Joaquin J. Torres, Samuel Johnson, Hilbert J. Kappen

      Pages 17-23

   4. Gradient Like Behavior and High Gain Design of KWTA Neural Networks

      • Daniela Danciu, Vladimir Răsvan

      Pages 24-32

   5. Fast Evaluation of Connectionist Language Models

      • F. Zamora-Martínez, M. J. Castro-Bleda, S. España-Boquera

      Pages 33-40

   6. Improving the Consistency of AHP Matrices Using a Multi-layer Perceptron-Based Model

      • Jose Antonio Gomez-Ruiz, Marcelo Karanik, José Ignacio Peláez

      Pages 41-48

   7. Global and Local Modelling in Radial Basis Functions Networks

      • L. J. Herrera, H. Pomares, I. Rojas, A. Guillén, G. Rubio, J. Urquiza

      Pages 49-56

   8. A Preliminar Analysis of CO2RBFN in Imbalanced Problems

      • M. D. Pérez-Godoy, A. J. Rivera, A. Fernández, M. J. del Jesus, F. Herrera

      Pages 57-64

   9. Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints

      • V. Van Belle, K. Pelckmans, J. A. K. Suykens, S. Van Huffel

      Pages 65-72

   10. A Simple Maximum Gain Algorithm for Support Vector Regression

       • Álvaro Barbero, José R. Dorronsoro

       Pages 73-80

   11. Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes

       • Julián Luengo, Francisco Herrera

       Pages 81-88

   12. Self-estimation of Data and Approximation Reliability through Neural Networks

       • Leonardo M. Reyneri, Valentina Colla, Mirko Sgarbi, Marco Vannucci

       Pages 89-96

   13. FPGA Implementations Comparison of Neuro-cortical Inspired Convolution Processors for Spiking Systems

       • A. Linares-Barranco, R. Paz, F. Gómez-Rodríguez, A. Jiménez, M. Rivas, G. Jiménez et al.

       Pages 97-105

3. Learning and Adaptation

   1. Nonparametric Location Estimation for Probability Density Function Learning

      • Ezequiel López-Rubio, Juan Miguel Ortiz-de-Lazcano-Lobato, María Carmen Vargas-González

      Pages 106-113

   2. An Awareness-Based Artificial Neural Network for Cooperative Distributed Environments

      • Mauricio Paletta, Pilar Herrero

      Pages 114-121

   3. Improving Classification under Changes in Class and Within-Class Distributions

      • Rocío Alaiz-Rodríguez, Alicia Guerrero-Curieses, Jesús Cid-Sueiro

      Pages 122-130

   4. Improving Training in the Vicinity of Temporary Minima

      • Ido Roth, Michael Margaliot

      Pages 131-139

   5. Convergence in an Adaptive Neural Network: The Influence of Noise Inputs Correlation

      • Adel Daouzli, Sylvain Saïghi, Michelle Rudolph, Alain Destexhe, Sylvie Renaud

      Pages 140-148

   6. Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA

      • M. Martínez-Zarzuela, F. J. Díaz Pernas, A. Tejero de Pablos, M. Antón Rodríguez, J. F. Díez Higuera, D. Boto Giralda et al.

      Pages 149-156

This volume presents the set of final accepted papers for the tenth edition of the IWANN conference “International Work-Conference on Artificial neural Networks” held in Salamanca (Spain) during June 10–12, 2009. IWANN is a biennial conference focusing on the foundations, theory, models and applications of systems inspired by nature (mainly, neural networks, evolutionary and soft-computing systems). Since the first edition in Granada (LNCS 540, 1991), the conference has evolved and matured. The list of topics in the successive Call for - pers has also evolved, resulting in the following list for the present edition: 1. Mathematical and theoretical methods in computational intelligence. C- plex and social systems. Evolutionary and genetic algorithms. Fuzzy logic. Mathematics for neural networks. RBF structures. Self-organizing networks and methods. Support vector machines. 2. Neurocomputational formulations. Single-neuron modelling. Perceptual m- elling. System-level neural modelling. Spiking neurons. Models of biological learning. 3. Learning and adaptation. Adaptive systems. Imitation learning. Reconfig- able systems. Supervised, non-supervised, reinforcement and statistical al- rithms. 4. Emulation of cognitive functions. Decision making. Multi-agent systems. S- sor mesh. Natural language. Pattern recognition. Perceptual and motor functions (visual, auditory, tactile, virtual reality, etc.). Robotics. Planning motor control. 5. Bio-inspired systems and neuro-engineering. Embedded intelligent systems. Evolvable computing. Evolving hardware. Microelectronics for neural, fuzzy and bio-inspired systems. Neural prostheses. Retinomorphic systems. Bra- computer interfaces (BCI). Nanosystems. Nanocognitive systems.

• agents
• ambient assisted living
• ambient intelligence
• artificial neural network
• bioinformatics
• cognition
• evolution
• evolutionary computation
• learning
• linguistics
• neural network
• optimization
• pattern recognition
• robot
• robotics

• Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación., , , ,, Barcelona, Spain

  Joan Cabestany

• Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Málaga, Spain

  Francisco Sandoval

• Department of Computer Architecture and Computer Technology, University of Granada, Spain

  Alberto Prieto

• Department of Informatics, University of Salamanca, Salamanca, Spain

  Juan M. Corchado

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