Description
Inductive Learning Algorithms for Complex Systems Modeling
Author: Madala H.R.
Language: EnglishSubjects for Inductive Learning Algorithms for Complex Systems Modeling:
Keywords
INDUCTIVE LEARNING ALGORITHM; Self-Organizing Data Analysis Techniques Algorithm; single-layer combinatorial algorithm; Optimal Connection Weights; complex systems modeling; Interpolation Interval; inductive learning algorithms; External Input Vector; Long Range Predictions; Self-organization Modeling; Finite Difference Equations; Combinatorial Algorithm; Residual MSE; Cellular Automata; Layered Network Structure; Complete Polynomial; Self-organization Clustering; Interpoint Distances; Minimum Bias; Criterion CV; Delayed Arguments; Input Output Matrix; LMS Algorithm; Finite Difference Analogues; Monthly Models; Full Model; Regularity Criterion; Consistency Criterion
· 17.8x25.4 cm · Hardback
Description
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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.