Inductive Learning Algorithms for Complex Systems Modeling
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€291.40
A01=H.R. Madala
advanced algorithmic modeling techniques
applied systems science
Author_H.R. Madala
Category=PBWH
Cellular Automata
Combinatorial Algorithm
Complete Polynomial
complex systems modeling
computational modeling methods
Consistency Criterion
Criterion CV
Delayed Arguments
environmental data analysis
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
External Input Vector
Finite Difference Analogues
Finite Difference Equations
Full Model
INDUCTIVE LEARNING ALGORITHM
inductive learning algorithms
Input Output Matrix
Interpoint Distances
Interpolation Interval
Layered Network Structure
LMS Algorithm
Long Range Predictions
Minimum Bias
Monthly Models
neural network clustering
Optimal Connection Weights
Regularity Criterion
Residual MSE
Self-organization Clustering
Self-organization Modeling
Self-Organizing Data Analysis Techniques Algorithm
self-organizing systems
single-layer combinatorial algorithm
time series prediction
Product details
- ISBN 9781315894393
- Weight: 860g
- Dimensions: 178 x 254mm
- Publication Date: 13 Dec 2017
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
