Inductive learning algorithms for complex systems modeling

It's a collection of fifty years of research in the area of inductive computer-aided algorithms, machine learning and advanced neural networks. The practical examples given in the book are superb. We have used the same concept in our data mining and pattern recognition works. The scripts given in the book with slight modification are very useful in building the networks. I suggest anybody in the specialization of neural networks, machine intelligence and data mining to use this abundent knowledge in their practical works. 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.

A monograph that surveys new types of learning algorithms for modelling complex scientific systems in science and engineering. The book features related discussions including algorithm development, structure and behaviour, and applications of various modelling activities.