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Prediction of arthritis using a modified Kohonen mapping and case based learning
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Comparing learning classifier systems and genetic programming: A case study
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The complex relationships between fibres, production parameters and spinning results
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Comparing Learning Classifier Systems and Genetic Programming: A Case Study.
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Soft Computing in Textiles: Present and Future.
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Optimising the Fibre-to-Yarn Production Process: Finding a Blend of Fibre Qualities to Create an Optimal Price/Quality Yarn.
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Digital printing for sampling
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FECS: an efficiency based Learning Classifier System applied to an industrial production process
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Genetic programming: principles and applications.
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Digital Printing for Sampling.
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Building a rule set for the fiber-to-yarn production process by means of soft computing techniques.
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FECS: an Efficiency based Learning Classifier System Applied to an Industrial Production Process.
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Distribution of neps in yarn
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An implementation of genetic algorithms for rule based machine learning.
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Modelling the spinning process by means of a fuzzy efficiency based classifier system
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Selection of the Best Cotton Blend: The Use of Advanced Techniques.
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Lerende systemen door middel van evolutionaire algoritmen
(1998) -
High Performant Learning Classifier Systems
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Using genetic algorithms to design a control strategy of an industrial process.
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Using genetic algorithms to design an overall control strategy of an industrial process
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Planning the Spinning Process by Means of Neural Networks and Genetic Algorithms
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Using Genetic Algorithms to Design an Overall Control Strategy of an Industrial Process
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Cotton Fibres Free of Contamination
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Het gebruik van artificiële intelligentie in textiel
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Learning Cognitive Systems for Modelling the Spinning Process
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Optical Sorter-Expert System for the Spinning Industry
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Optimizing the fiber-to-yarn production process with a combined neural network genetic algorithm approach.
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Optimalisering van het vezel-garen proces door middel van genetische algoritmen.
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Optimisation of the fibre-to-yarn process using genetic algorithms.
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Faut Detection and Quality Assessment in Textiles by Means of Neural Nets
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Optimisation of the Fibre-to-Yarn Process using Genetic Algorithms
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Automatic assessment of carpet wear using image analysis and neural networks.
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Optimising a production process by a neural network genetic algorithm approach.
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Modelling relaxation behaviour of yarns .2. Back propagation neural network model.
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The use of neural nets to predict yarn tensile properties.
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Fault Detection and Qualtiy Assessment in Textiles by means of Neural Nets.
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USE OF NEURAL NETS FOR DETERMINING THE SPINNABILITY OF FIBERS.
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Comments on 'Using Neural Networks to Predict Dye Concentrations in Multiple-Dye Mixtures
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Using neural networks to predict dye concentrations in multiple-dye mixtures - comment.
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Learning Classifier Systems for Modelling the Spinning Process. 24th International Cotton Conference Bremen, Faserinstitut e.V., Bremen, 1998, pp. 179-185.
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The Complex Relatinships Between Fibres, Production Parameters and Spinning Results