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This book mainly focuses on the multi-media energy prediction
technology and optimization methods of iron and steel enterprises.
The technical methods adopted include swarm intelligence algorithm,
neural network, reinforcement learning, and so on. Energy saving
and consumption reduction in iron and steel enterprises have always
been a research hotspot in the field of process control. This book
considers the multi-media energy balance problem from the
perspective of system, studies the energy flow and material flow in
iron and steel enterprises, and provides energy optimization
methods that can be used for planning, prediction, and scheduling
under different production scenes. The main audience of this book
is scholars and graduate students in the fields of control theory,
applied mathematics, energy optimization, etc.
This book focuses on the performance optimization of fault
diagnosis methods for power systems including both model-driven
ones, such as the linear parameter varying algorithm, and
data-driven ones, such as random matrix theory. Studies on fault
diagnosis of power systems have long been the focus of electrical
engineers and scientists. Pursuing a holistic approach to improve
the accuracy and efficiency of existing methods, the underlying
concepts toward several algorithms are introduced and then further
applied in various situations for fault diagnosis of power systems
in this book. The primary audience for the book would be the
scholars and graduate students whose research topics including the
control theory, applied mathematics, fault detection, and so on.
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