Practical Applications of Computational Intelligence Techniques

1. An introduction to computational intelligence paradigms.- 1 Computational intelligence – a formal definition.- 2 The logic of fuzzy sets.- 3 Computational models of neural nets.- 4 Genetic algorithms.- 5 Belief networks.- 6 Computational learning theory.- 7 Synergism of the computational intelligence paradigms.- 8 Conclusions and future directions.- References.- 2. Networked virtual park.- 1 Introduction.- 2 The attraction builder.- 3 Networked virtual environment system.- 4 The server.- 5 Conclusion.- Acknowledgements.- References.- 3. Commercial coin recognisers using neural and fuzzy techniques.- 1 Introduction.- 2 Problem analysis and database compilation.- 3 Approach using artificial neural networks models.- 4 Approach using fuzzy logic models.- 5 Conclusions.- References.- 4. Fuzzy techniques in intelligent household appliances.- 1 Introduction.- 2 Fuzzy approaches for intelligent devices.- 3 Introducing fuzziness to kitchen oven.- 4 Refrigerator-freezer control using fuzzy logic.- 5 Model and simulation of refrigerating-freezing appliance using one compressor.- 6 Hardware implementation.- 7 Decrease of energy consumption from national point-of-view.- 8 Conclusion.- References.- 5. Neural prediction in industry: increasing reliability through use of confidence measures and model combination.- 1 Introduction.- 2 Paper curl prediction.- 3 Neural network model development.- 4 Model combination.- 5 Confidence measures.- 6 Results.- 7 Discussion.- Acknowledgments.- References.- 6. Handling the back calculation problem in aerial spray models using a genetic algorithm.- 1 Introduction.- 2 Early spray models.- 3 Genetic algorithms.- 4 Development of Fortran-SAGA.- 5 Development of VB-SAGA 1.0.- 6 Development of VB-SAGA 2.0.- 7 Summary and conclusions.- References.-7. Genetic algorithm optimization of a filament winding process modeled in WITNESS.- 1 Introduction.- 2 Filament winding model.- 3 Genetic algorithm interface.- 4 Results.- 5 Conclusions.- 6 Summary.- Acknowledgments.- References.- 8.Genetic algorithm for optimizing the gust loads for predicting aircraft loads and dynamic response.- 1 Introduction.- 2 Problem statement and related mathematical underpinnings.- 3 An approach using genetic algorithm.- 4 Results of approach on linear aircraft model.- 5 Summary and conclusion.- Acknowledgments.- References.- 9. A stochastic dynamic programming technique for property market timing.- 1 Introduction.- 2 Review of theoretical considerations.- 3 Specification of market timing model.- 4 Stochastic dynamic programming.- 5 Data used in the simulation study.- 6 Performance and evaluation tests.- 7 Conclusions.- References.- 10. A hybrid approach to breast cancer diagnosis.- 1 Introduction.- 2 KBANNs.- 3 Metabolic features of cancerous breast tissues.- 4 Knowledge elicitation and refinement.- 5 31P MRS data.- 6 KBANN topology.- 7 Results.- 8 Conclusions.- Acknowledgements.- References.- 11. Artificial neural networks as a computer aid for lung disease detection and classification in ventilation-perfusion lung scans.- 1 Introduction.- 2 Artificial neural networks.- 3 Materials and methods.- 4 Results.- 5 Discussion.- Acknowledgments.- References.- 12. Neural network for classification of focal liver lesions in ultrasound images.- 1 Introduction.- 2 Texture analysis of focal liver lesions by neural networks.- 3 Experimental results.- 4 Discussion.- 5 Conclusions.- Acknowledgments.- Entropy.- Root mean square (RMS) variation.- First moment of power spectrum.- References.