This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans.

The text is suitable for senior/graduate courses in neural networks and adaptive filters.

All derivations are presented step-by-step. This new fifth edition has been fully revised and includes a new chapter, a range of new sections, figures and exercises. The Solution chapter has been reorganized for clarity.

This is a true guidebook to the world of complex nonlinear phenomena. (Ilya Pavlyukevich, Zentralblatt MATH, Vol. 1146, 2008) Claudius Gros’ Complex and Adaptive Dynamical Systems: A Primer is a welcome addition to the literature.

The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments.

The text features benchmark problems throughout to offer insights and illustrate some of the computational implications.

Written by two acknowledged experts in this emerging field, this book offers researchers their first comprehensive introduction to adaptive sampling.

... Adaptive Learning (FBOAL) for the collocation points in PINNs that adds and removes points based on PDEs residuals ... methods of adaptive sampling for the collocation points such as Residual-based Adaptive Distribution (RAD) and ...

... adaptive sampling approach to the frequency domain model reduction methods, such as multi-moment matching [8], in ... Learning?: With Applications in R. Springer Texts in Statistics, vol. 103. Springer, New York (2013) 14. Maday, Y ...

... systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task ... Adaptive Parallel/Serial Sampling Mechanisms for Particle Filtering in Dynamic Bayesian Networks Introduction.