Unique in its systematic approach to stochastic systems, this book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces.

The Pattern Recognition in Bioinformatics (PRIB) meeting was established in 2006 under the auspices of the International Association for Pattern Recognition (IAPR) to create a focus for the development and application of pattern recognition techniques in the biological domain. PRIB's aim to explore the full spectrum of pattern recognition application was re?ected in the breadth of techniquesrepresented in this year's subm- sions and in this book. These range from image analysis for biomedical data to systems biology. We werefortunatetohaveinvitedspeakersofthehighestcalibredeliveringkeynotes at the conference. These were Pierre Baldi (UC Irvine), Alvis Brazma (EMBL-EBI), GunnarRats .. ch(MPITubi .. ngen)andMichaelUnser(EPFL).Weacknowledgesupportof theEUFP7NetworkofExcellencePASCAL2forpartiallyfundingtheinvitedspeakers. Immediately prior to the conference, we hosted half day of tutorial lectures, while a special session on "Machine Learningfor IntegrativeGenomics" was held immediately after the main conference.Duringthe conference,a poster session was heldwith further discussion. Wewouldlikeonceagaintothankalltheauthorsforthehighqualityofsubmissions, as well as Yorkshire South and the University of Shef?eld for providing logistical help in organizing the conference. Finally, we would like to thank Springer for their help in assembling this proceedings volume and for the continued support of PRIB.

This book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces. The underlying design philosophy is based on effecting closed-loop control in the presence of plant or environmental uncertainty and complexity by utilizing various types of neural network architectures, ranging from multilayer perceptron to radical basis function and modular network models. The uncertainty and complexity are typified by unknown nonlinear functionals, and temporal or spatial multi-modality. Deterministic and stochastic conditions, as well as continuous and discrete time dynamics are taken into consideration. The presented designs are firmly rooted in the techniques of adaptive control, reconfigurable control, multiple model control, stochastic adaptive control, lyapunov stability theory and neural networks. The techniques are shown to enhance the performance of the control system in the presence of the higher levels of complexity and uncertainty associated with modern plants, which demand superior intelligence and autonomy from the controller. The presented designs are supported both by theory and by numerous results from simulation experiments. The book also includes extensive reviews on general aspects concerning the fields of intelligent, nonlinear and stochastic control.

This authored monograph presents the use of dynamic spatiotemporal modeling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. The authors use ideas from statistics, signal processing, and ecology, and provide a predictive framework which is able to assimilate data and give confidence estimates on the predictions. The book also demonstrates the methods on the WikiLeaks Afghan War Diary, the results showing that this approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from preceding years. The target audience primarily comprises researchers and practitioners in the involved fields but the book may also be beneficial for graduate students.

This book is a practical introduction to the safe operation and control of critical systems in defense, industrial, and healthcare applications. It highlights system engineering processes and presents an overview of the equipment health monitoring (EHM) functional architecture and algorithm design. The book also explores machine learning functions, such as feature extraction, data visualization and model boundaries. The need for intelligent diagnostics and proposed health monitoring framework is increasingly important within sensing technology, big data analytics and grid capabilities. This resource, packed with case studies from industrial and healthcare settings, identifies key problems along with various techniques that address the current issues as well as future developments in the field. A MATLAB code is included to assist engineers with projects in the field.