图书简介
Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series. Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo-Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc.It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering.
1. Ant-Colony Based Simulation-Optimization Modeling 2. Artificial Intelligent and Deep Learning 3. Artificial Neural Network 4. Bat Algorithm 5. Citizen Science 6. Conceptual Grey 7. Data Reduction Techniques 8. Data Science for Utilities and Urban Systems 9. Decision Tree Algorithm 10. Discrete Mixed Subdomain Least Squares 11. Earthen Worm Algorithm 12. Entropy and Resilience Indices 13. Evolutionary Based Meta-Modeling 14. Evolutionary Polynomial Regression Paradigm 15. Firefly Algorithm 16. Fish-Friendly Engineering (Fish Habitat Analysis) 17. Fuzzy Logic 18. Game Theory 19. Genetic Algorithm 20. Gene Expression Models 21. Heuristic Burst Detection Method 22. Honey-Bee Mating Optimization 23. Hybrid Cuckoo-Harmony Search Algorithm 24. Hybrid Mechanistic Data Driven Model 25. Imperialist Competitive Algorithm 26. Integrated Cellular Automata Evolution 27. Lattice Boltzmann Method 28. Meshless Particle Modeling 29. Multivariare Regressions 30. Ontology-Based Knowledge Management framework 31. Random Forest 32. Relevance Vector Machine 33. Rhie and Chow Interpolation 34. Supporting Vector Machine
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