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H.R. Madala, Alexy G. Ivakhnenko, "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.

Chapter 1 is concerned with the basic approach of induction and the principle of self-organization. The selection criteria and general features of the algorithms are described.

Chapter 2 considers various inductive learning algorithms: multilayer, single-layered combinatorial, multi-layered aspects of combinatorial, multi-layered with propagating residuals, harmonical algorithms, and some new algorithms like correlational and orthogonalized partial descriptions. The authors also describe the scope of long-range quantitative predictions and levels of dialogue language generalization with subjective versus multilevel objective analysis.

Chapter 3 covers noise immunity of algorithms in analogy with information theory. Various selection criteria, their classification and analysis, the aspects of the asymptotic properties of external criteria, and the convergence of algorithms are also described.

Chapter 4 concentrates on the description of physical fields and their representation in the finite-difference schemes, as these are important in complex systems modeling. The model formulations of cyclic processes is also explained.

Chapter 5 coverage is on how unsupervised learning or clustering might be carried out with the inductive type of learning technique. The development of new algorithms like objective computerized clustering (OCC) is presented in detail.

Chapter 6 takes up some of the applications related to complex systems modeling such as weather modeling, ecological, economical, agricultural system studies, and modeling of solar activity. The main emphasis of the chapter is on how to use specific inductive learning algorithms in a practical situation.

Chapter 7 addresses application of inductive learning networks in comparison with the artificial neural networks that work on the basis of averaged output error. The least mean-square (LMS) algorithm (adaline), backpropagation, and self-organization boolean-logic techniques are considered. Various simulation results are presented. One notes that the backpropagation technique which is encouraged by many scientists, is only one of several possible ways to solve the systems of equations to estimate the connection coefficients of a feed-forward network.

Chapter 8 presents the computational aspects of basic inductive learning algorithms. Although an interactive software package for inductive learning algorithms which includes multilayer and combinatorial algorithms was recently released as a commercial package, the basic source of these algorithms along with the harmonical algorithm are given in chapter 8.

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.

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