Grammatical Evolution

Sample

Vladimir S. Lerner

The Variation Principle In Informational Macrodynamics (VP) introduces the process' integral information measure, which has a distinctive difference from traditional information approaches that use an entropy function. The VP's minimax mathematical mechanisms formalize the regularities of the cooperative informational dynamics, connecting randomness and regularities, stochastic and determinism, reversibility and irreversibility, symmetry and nonsymmetry, stability and instability, regular and chaotic dynamics, thermodynamics and informational dynamics, time-reversible and time-irreversible processes, reveals the information dynamic mechanisms of evolution and development. The mathematical formalisms and methods, implemented in the forms of computer models, algorithms and programs, provide new general tools for Information Systems' Modeling in such areas as biology, intelligent systems, computer and information technology, including communications, data modeling and data management. Variation Principle In Informational Macrodynamics, with its examples and applications, will meet the needs of a professional audience composed of researchers and practitioners in industry, as well as graduate-level students in computer science, mathematics and engineering.

Sample

Alan F. Murray

Applications of Neural Networks gives a detailed description of 13 practical applications of neural networks, selected because the tasks performed by the neural networks are real and significant. The contributions are from leading researchers in neural networks and, as a whole, provide a balanced coverage across a range of application areas and algorithms. The book is divided into three sections. Section A is an introduction to neural networks for nonspecialists. Section B looks at examples of applications using `Supervised Training'. Section C presents a number of examples of `Unsupervised Training'. For neural network enthusiasts and interested, open-minded sceptics. The book leads the latter through the fundamentals into a convincing and varied series of neural success stories -- described carefully and honestly without over-claiming. Applications of Neural Networks is essential reading for all researchers and designers who are tasked with using neural networks in real life applications.

Sample

Robert J. Jannarone

Many monitoring, forecasting, and control operations occur in settings where relationships among key measurements must be learned quickly. Examples are on-line industrial processes where influent material is not consistent over time, energy load or price forecasting where demand characteristics change rapidly,and health management where relationships among monitored variables must be learned for each patient-treatment combination. The solution presented is a new neuro-computing system that learns in real-time, even when data arrival rates are several million measurements per second. The book describes benefits and features of the system, statistical foundations for the system, and several related models. The book also describes available system software.

Sample

William F. Clocksin, Christopher S. Mellish

Since the first edition of this book in 1981, Prolog has continued to attract an unexpectedly great deal of interest in the computer science community and has turned out to be a basis for an important new family of programming languages and systems for Artificial Intelligence. In the preceding three editions, the authors have steadily added new material, improved the presentation, and corrected various minor errors to provide a textbook as well as a reference work for everyone who wants to study and use Prolog as a practical programming language. The authors concentrate on teaching "core" Prolog. All examples conform to this standard and will run on the most widely-used Prolog implementations some of which are listed in the appendices with indications as to how they diverge from the standard.