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Modelling and Simulation : Exploring Dynamic System Behaviour
Modelling and Simulation: Exploring Dynamic System Behaviour provides the reader with a balanced and integrated presentation of the modelling and simulation activity for both Discrete Event Dynamic Systems (DEDS) and Continuous Time Dynamic Systems (CTDS). This book presents the fundamentals necessary to understand the many important facets of the modeling and simulation methodology.
Modelling and Reasoning with Vague Concepts
This volume outlines a formal representation framework for modelling and reasoning with vague concepts in Artificial Intelligence. The new calculus has many applications, especially in automated reasoning, learning, data analysis and information fusion. This book gives a rigorous introduction to label semantics theory, illustrated with many examples, and suggests clear operational interpretations of the proposed measures. It also provides a detailed description of how the theory can be applied in data analysis and information fusion based on a range of benchmark problems.
Flexible and Efficient Information Handling ; 23rd British National Conference on Databases, BNCOD 23, Belfast, Northern Ireland, UK, July 18-20, 2006, Proceedings
This book constitutes the refereed proceedings of the 23rd British National Conference on Databases, BNCOD 23, held in Belfast, Northern Ireland, July 2006. The volume presents 12 revised full papers and 6 revised short papers, together with 2 invited lectures and 13 poster papers. Topical sections include data modelling and architectures and transaction management, data integration and interoperability and information retrieval, query processing and optimisation, data mining, data warehousing and more.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
