Graphs are extremely useful in modeling systems in physical sciences and engineering problems, because of their intuitive diagrammatic nature. This text gives a reasonably deep account of material closely related to engineering applications. Topics like directed-graph solutions of linear equations, topological analysis of linear systems, state equations, rectangle dissection and layouts, and network flows are included. A major theme of the book is electrical network theory. This book is basically intended as a reference text for researchers, and requires a certain level of mathematical maturity. However the text may equally well be used for graduate level courses on network topology and linear systems and circuits. Some of the later chapters are suitable as topics for advanced seminars. A special feature of the book is that references to other published literature are included for almost all the results presented, making the book especially handy for those wishing to continue with a study of special topics.

Includes bibliographical references and index.

This book constitutes the refereed proceedings of the 13th International Conference on Engineering Applications of Neural Networks, EANN 2012, held in London, UK, in September 2012. The 49 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers describe the applications of neural networks and other computational intelligence approaches to intelligent transport, environmental engineering, computer security, civil engineering, financial forecasting, virtual learning environments, language interpretation, bioinformatics and general engineering.

Adding one and one makes two, usually. But sometimes things add up to more than the sum of their parts. This observation, now frequently expressed in the maxim “more is different”, is one of the characteristic features of complex systems and, in particular, complex networks. Along with their ubiquity in real world systems, the ability of networks to exhibit emergent dynamics, once they reach a certain size, has rendered them highly attractive targets for research. The resulting network hype has made the word “network” one of the most in uential buzzwords seen in almost every corner of science, from physics and biology to economy and social sciences. The theme of “more is different” appears in a different way in the present v- ume, from the viewpoint of what we call “adaptive networks.” Adaptive networks uniquely combine dynamics on a network with dynamical adaptive changes of the underlying network topology, and thus they link classes of mechanisms that were previously studied in isolation. Here adding one and one certainly does not make two, but gives rise to a number of new phenomena, including highly robust se- organization of topology and dynamics and other remarkably rich dynamical beh- iors.