Discusses algorithm development, structure, and behaviorPresents comprehensive coverage of algorithms useful for complex systems modelingIncludes recent studies on clusterization and recognition problemsProvides listings of algorithms in FORTRAN that can be run directly on IBM-compatible PCs
Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and engineering. The book features discussions of algorithm development, structure, and behavior; comprehensive coverage of all types of algorithms useful for this subject; and applications of various modeling activities (e.g., environmental systems, noise immunity, economic systems, clusterization, and neural networks). It presents recent studies on clusterization and recognition problems, and it includes listings of algorithms in FORTRAN that can be run directly on IBM-compatible PCs.
Inductive Learning Algorithms for Complex Systems Modeling will be a valuable reference for graduate students, research workers, and scientists in applied mathematics, statistics, computer science, and systems science disciplines. The book will also benefit engineers and scientists from applied fields such as environmental studies, oceanographic modeling, weather forecasting, air and water pollution studies, economics, hydrology, agriculture, fisheries, and time series evaluations.
Table of Contents
1. Introduction: Systems and Cybernetics 2. Inductive Learning Algorithms: Self-Organization Method 3. Network Structures 4. Long Term Quantitative Predictions 5. Dialogue Language Generalization 6. Noise Immunity and Convergence: Analogy with Information Theory 7. Classification and Analysis of Criteria. Improvement of Noise Immunity 8. Asymptotic Properties of Criteria 9. Balance Criterion of Predictions 10. Convergence of Algorithms 11. Physical Fields and Modeling: Finite-Difference Pattern Schemes 12. Comparative Studies 13. Cyclic Processes 14. Clusterization and Recognition: Self-Organization Modeling and Clustering 15. Methods of Self-Organization Clustering 16. Objective Computer Clustering Algorithm 17. Levels of Discretization and Balance Criterion 18. Forecasting Methods of Analogues 19. Applications: Fields of Application 20. Weather Modeling 21. Ecological System Studies 22. Modeling of Economical Systems 23. Agricultural System Studies 24. Modeling of Solar Activity 25. Inductive and Deductive Networks: Self-Organization Mechanism in the Networks 26. Network Techniques 27. Generalization 28. Comparison and Simulation Results 29. Basic Algorithms and Program Listings: Computational Aspects of Multilayered Algorithm 30. Computational Aspects of Combinatorial Algorithm 31. Computational Aspects of Harmonical Algorithm.