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In the era of continuous changes in internal organizationalsettings and external business environments – such as new regulations and business opportunities – modern enterprises are subject to extensive research and study. For the understanding, design, and engineering of modern enterprises and theircomplexbusiness processes,thedisciplineofenterpriseengineeringrequires sound engineering principles and systematic approaches based on rigorous th- ries. Along with that, a paradigm shift seems to be needed for addressing these issues adequately. The main paradigm shift is the consideration of an enterprise and its business processes as a social system. In its social setting, an enterprise and its business processes represent actors with certain authorities and assigned roles, who assume certain responsibilities in order to provide a service to its environment. Second to that, a paradigm shift is to look at an enterprise as an artifact purposefully designed for a certain mission and goal. The need for this paradigm shift, along with the complexity and agility of modern enterprises, gives inspiration for the emerging discipline of enterprise engineering that requires development of new theories and methodologies. To this end, the prominent methods and tools of modeling and simulation play a signi?cant role. Both (conceptual) modeling and simulation are widely used for understanding, analyzing, and engineering an enterprise (its organization and business processes).

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A cursory glance at the table of contents of EANN 2009 reveals the am- ing range of neural network and related applications. A random but revealing sample includes: reducing urban concentration, entropy topography in epil- tic electroencephalography, phytoplanktonic species recognition, revealing the structure of childhood abdominal pain data, robot control, discriminating angry and happy facial expressions, ?ood forecasting, and assessing credit worthiness. The diverse nature of applications demonstrates the vitality of neural comp- ing and related soft computing approaches, and their relevance to many key contemporary technological challenges. It also illustrates the value of EANN in bringing together a broad spectrum of delegates from across the world to learn from each other’s related methods. Variations and extensions of many methods are well represented in the proceedings, ranging from support vector machines, fuzzy reasoning, and Bayesian methods to snap-drift and spiking neurons. This year EANN accepted approximately 40% of submitted papers for fu- length presentation at the conference. All members of the Program Committee were asked to participate in the reviewing process. The standard of submissions was high, according to the reviewers, who did an excellent job. The Program and Organizing Committees thank them. Approximately 20% of submitted - pers will be chosen, the best according to the reviews, to be extended and - viewedagainfor inclusionin a specialissueofthe journalNeural Computing and Applications. We hope that these proceedings will help to stimulate further research and development of new applications and modes of neural computing.

Neural networks (Computer science) --Congresses. --- Systems engineering --Congresses. --- Neural networks (Computer science) --- Systems engineering --- Computer Science --- Engineering & Applied Sciences --- Biologically-inspired computing --- Bio-inspired computing --- Natural computing --- Computer science. --- Computers. --- Artificial intelligence. --- Computer simulation. --- Pattern recognition. --- Biometrics (Biology). --- Engineering. --- Computer Science. --- Computation by Abstract Devices. --- Artificial Intelligence (incl. Robotics). --- Engineering, general. --- Pattern Recognition. --- Simulation and Modeling. --- Biometrics. --- Bionics --- Electronic data processing --- Optical pattern recognition. --- Artificial Intelligence. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Construction --- Industrial arts --- Technology --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Statistical methods --- Pattern recognition systems. --- Biometric identification. --- Theory of Computation. --- Technology and Engineering. --- Automated Pattern Recognition. --- Computer Modelling. --- Biometric person authentication --- Biometrics (Identification) --- Anthropometry --- Identification --- Pattern classification systems --- Pattern recognition computers --- Computer vision