Artificial Neural Nets and Genetic Algorithms

1. Front Matter

   Pages I-X

   PDF

2. Plenary talks

   1. Topological Approach to Fuzzy Sets and Fuzzy Logic

      • R. F. Albrecht

      Pages 1-7

   2. Linking Human Neural Processes Directly with Technology — The Future?

      • K. Warwick

      Pages 8-13

   3. Neural Dynamic Model for Optimization of Complex Systems

      • Hojjat Adeli

      Pages 14-15

3. Neural Networks – Theory and Applications

   1. Comparative Testing of Hyper-Planar Classifiers on Continuous Data Domains

      • David McLean, Zuhair Bandar

      Pages 16-21

   2. A Quantum Associative Memory Based on Grover’s Algorithm

      • Dan Ventura, Tony Martinez

      Pages 22-27

   3. Newton Filters: a New Class of Neuron-Like Discrete Filters and an Application to Image Processing

      • Alexis Quesada-Arencibia, Miguel Alemán-Flores, Roberto Moreno-Diaz Jr.

      Pages 28-34

   4. Improving Generalisation Using Modular Neural Networks

      • David McLean, Zuhair Bandar, Jim O’Shea

      Pages 35-39

   5. The Role and Modelling of Presynaptic Inhibition in the Visual Pathway: Applications in Image Processing

      • Roberto Moreno-Díaz Jr., Alexis Quesada-Arencibia

      Pages 40-45

   6. A Curvature Primal Sketch Neural Network Recognition System

      • Michael Fairbank, Andrew Tuson

      Pages 46-51

   7. Using GMDH Neural Net and Neural Net With Switching Units to Find Rare Particles

      • František Hakl, Marcel Jiřina

      Pages 52-58

   8. A Neural Network Based Nonlinear Temporal-Spatial Noise Rejection System

      • Fa-Long Luo, Rolf Unbehauen, Tertulien Ndjountche

      Pages 59-66

   9. Improving the Performance of the Hopfield Network By Using A Relaxation Rate

      • Xinchuan Zeng, Tony R. Martinez

      Pages 67-72

   10. A New Activation Function in the Hopfield Network for Solving Optimization Problems

       • Xinchuan Zeng, Tony R. Martinez

       Pages 73-77

   11. On-line Learning of Object Representations

       • Horst Bischof, Aleš Leonardis

       Pages 78-85

   12. A neural network model for blast furnace wall temperature pattern classification

       • Henrik Saxén, Leif Lassus

       Pages 86-88

   13. Monitoring an Industrial Plastic Injection Moulding Machine Using Neural Networks

       • N. Costa, A. Cunha, B. Ribeiro

       Pages 89-94

   14. Rates of Learning in Gradient and Genetic Training of Recurrent Neural Networks

       • Ricardo Riaza, Pedro J. Zufiria

       Pages 95-99

   15. Influence of the Learning Gain on the Dynamics of Oja’s Neural Network

       • Pedro J. Zufiria

       Pages 100-106

   16. ATR’s Artificial Brain Project: CAM-Brain Machine (CBM) and Robot Kitten (Robokoneko) Issues

       • Michael Korkin, Hugo de Garis, N. Eiji Nawa, William Dee Rieken

       Pages 107-110

From the contents: Neural networks – theory and applications: NNs (= neural networks) classifier on continuous data domains– quantum associative memory – a new class of neuron-like discrete filters to image processing – modular NNs for improving generalisation properties – presynaptic inhibition modelling for image processing application – NN recognition system for a curvature primal sketch – NN based nonlinear temporal-spatial noise rejection system – relaxation rate for improving Hopfield network – Oja's NN and influence of the learning gain on its dynamics Genetic algorithms – theory and applications: transposition: a biological-inspired mechanism to use with GAs (= genetic algorithms) – GA for decision tree induction – optimising decision classifications using GAs – scheduling tasks with intertask communication onto multiprocessors by GAs – design of robust networks with GA – effect of degenerate coding on GAs – multiple traffic signal control using a GA – evolving musical harmonisation – niched-penalty approach for constraint handling in GAs – GA with dynamic population size – GA with dynamic niche clustering for multimodal function optimisation Soft computing and uncertainty: self-adaptation of evolutionary constructed decision trees by information spreading – evolutionary programming of near optimal NNs

• algorithms
• classification
• genetic algorithms
• image processing
• learning
• modeling
• optimization
• proving
• sketch
• uncertainty
• complexity

• Fakulteta za Računalništvo in Informatiko, Univerza v Ljubljani, Ljubljana, Slovenia

  Andrej Dobnikar

• Division of Mathematics, School of Mathematical and Information Sciences, Coventry University, Coventry, UK

  Nigel C. Steele

• Laboratoire de Génie Informatique et Ingénierie de Production, Ecole pour les Etudes et la Recherche en Informatique et Electronique, Nimes, France

  David W. Pearson

• Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

  Rudolf F. Albrecht

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