Cyber-Physical Systems (CPSs) represent an emerging research area that has attracted the attention of many researchers and are currently of interest in academia, industry, and government due to their potentially significant impact on society, environment, and economy.
In general, CPS refers to the next generation of engineered systems that require tight integration of computing, communication, and control technologies to achieve stability, performance, reliability, robustness, and efficiency in dealing with physical systems of many application domains such as transportation, energy, medical, and defense. Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and a myriad of other engineered socio-technical systems, we must consider the safety of systems involving machine learning.
Machine Learning for Cyber Physical Systems covers the latest trends and innovations in the field. It studies fundamental machine learning algorithms in supervised and unsupervised manners and examines new computing architecture for the development of next generation CPS. Important applications of CPS are also covered in this book. Particularly, regarding supervised machine learning algorithms, several generative learning and discriminative learning methods are proposed to improve learning performance. It has also been seen as the future of information technology which will transform how people interact with the physical world, just as the internet transformed how people interacted with each other. Toward intelligent CPS, it is necessary to incorporate computational intelligence into physical processes, adding new capacities to the systems such as safety, efficiency and productivity.