Madala H.R., Ivakhnenko A.G. Inductive Learning Algorithms for Complex Systems Modeling

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 app lied 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.FeaturesDiscusses algorithm development, structure, and behavior
Presents comprehensive coverage of algorithms useful for complex systems modeling
Includes recent studies on clusterization and recognition problems
Provides listings of algorithms in FORTRAN that can be run directly on IBM-compatible
PCs