Artificial Neural Nets and Genetic Algorithms

1. Applications in Biology and Biotechnology

   1. The Costing of Process Vessels Using Neural Networks

      • M. Leck, P. Bromley, D. Peel, A. M. Gerrard

      Pages 69-71

   2. Neural Network Modelling of Fermentation Taking into Account Culture Memory

      • P. Koprinkova, M. Petrova, T. Patarinska

      Pages 72-75

   3. Brain Electrographic State Detection Using Combined Unsupervised and Supervised Neural Networks

      • A. J. F. Coimbra, J. Marino-Neto, F. M. de Azevedo, C. G. Freitas, J. M. Barreto

      Pages 76-79

2. Genetic and Neural Theory Combined

   1. From the Chromosome to the Neural Network

      • Olivier Michel, Joëlle Biondi

      Pages 80-83

   2. CAM-Brain

      • Hugo de Garis

      Pages 84-87

   3. A Perfect Integration of Neural Networks and Evolutionary Algorithms

      • Percy P. C. Yip, Yoh-Han Pao

      Pages 88-91

   4. g-lvq, a combination of genetic algorithms and lvq

      • J. J. Merelo, A. Prieto

      Pages 92-95

   5. Evolving Neural Network Structures: An Evaluation of Encoding Techniques

      • Stephen G. Roberts, Mike Turega

      Pages 96-99

3. Failure Detection and Identification

   1. Application of Radial Basis Function Networks to Fault Diagnosis for a Hydraulic System

      • Dingli Yu, D. N. Shields, S. Daley

      Pages 100-103

   2. Optimally Robust Fault Diagnosis Using Genetic Algorithms

      • Dingli Yu, D. N. Shields

      Pages 104-107

   3. Development of a Neural-based Diagnostic System to Control the Ropes of Mining Shifts

      • F Ortega, J. B Ordieres, C Menéndez, C. González Nicieza

      Pages 108-111

   4. Lime Kiln Fault Detection and Diagnosis by Neural Networks

      • B Ribeiro, E Costa, A Dourado

      Pages 112-115

   5. Investigations into the Use of Wavelet Transformations as Input into Neural Networks for Condition Monitoring

      • J MacIntyre, J C O’Brien

      Pages 116-119

4. Image, Motion and Recognition

   1. Genetic Algorithm for Neurocomputer Image Recognition

      • Ernst M. Kussul, Tatyana N. Baidyk

      Pages 120-123

   2. Feature Map Architectures for Pattern Recognition: Techniques for Automatic Region Selection

      • B. Martín-del-Brío, N. Medrano-Marqués, J. Blasco-Alberto

      Pages 124-127

   3. Adaptive Genetic Algorithms for Multi-Point Path Finding in Artificial Potential Fields

      • R. M. Rylatt, C. A. Czarnecki, T. W. Routen

      Pages 128-131

   4. Spatio-Temporal Mask Learning: Application to Speech Recognition

      • Stéphane Durand, Frédéric Alexandre

      Pages 132-135

   5. Artificial Neural Networks for Motion Estimation

      • Olaf Schnelting, Bernd Michaelis, Rüdiger Mecke

      Pages 136-139

   6. Advanced Neural Networks Methods for Recognition of Handwritten Characters

      • Sławomir Skoneczny, Jarosław Szostakowski

      Pages 140-143

5. Genetic Algorithms Theory

   1. The Use of a Variable Length Chromosome for Permutation Manipulation in Genetic Algorithms

      • Phil Robbins

      Pages 144-147

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.

• agents
• algorithms
• artificial neural network
• control
• fuzzy logic
• genetic algorithms
• learning
• modeling
• multimedia
• networks
• neural networks
• optimization
• pattern recognition
• simulation
• speech recognition
• complexity

• Laboratoire de Génie Informatique et Ingénierie de Production, Ecole pour les Etudes et la Recherche en Informatique et Electronique (EMA-EERIE), Nîmes, France

  David W. Pearson

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

  Nigel C. Steele

• Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

  Rudolf F. Albrecht

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