Christian Moewes – författare

Complex systems and their phenomena are ubiquitous as they can befound in biology, finance, the humanities, management sciences,medicine, physics and similar fields.

For many problems in these fields, there are no conventional ways tomathematically or analytically solve them completely at low cost. Onthe other hand, nature already solved many optimization problemsefficiently. Computational intelligence attempts to mimicnature-inspired problem-solving strategies and methods.

These strategies can be used to study, model and analyze complexsystems such that it becomes feasible to handle them. Key areas ofcomputational intelligence are artificial neural networks,evolutionary computation and fuzzy systems.

As only a few researchers in that field, Rudolf Kruse has contributedin many important ways to the understanding, modeling and applicationof computational intelligence methods. On occasion of his 60thbirthday, a collection of original papers of leading researchers inthe field of computational intelligence has been collected in thisvolume.

In recent years there has been a growing interest to extend classical methods for data analysis.The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance.Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled.About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS).

This book gathers contributions presented at the SMPS''2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis.It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics.Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.

Die Autoren behandeln umfassend zentrale Themen der Informatik von Künstlichen Neuronalen Netzen, über Evolutionäre Algorithmen bis hin zu Fuzzy-Systemen und Bayes-Netzen. Denn: Der Anwendungsbereich „Computational Intelligence“ erlangt durch viele erfolgreiche industrielle Produkte immer mehr an Bedeutung. Dieses Buch behandelt die zentralen Techniken dieses Gebiets und bettet sie in ein didaktisches Konzept ein, welches sich gezielt an Studierende und Lehrende der Informatik wendet. Für die vorliegende 2. Auflage des Buches wurden alle Themenbereiche überarbeitet, aktualisiert und zum Teil erweitert.

Zusatzmaterialen wie Aufgaben, Lösungen und Foliensätze für Vorlesungen sowie Beispiele aus der industriellen Anwendung betonen den praktischen Charakter des Buches.