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The book offers an insight on artificial neural networks for giving
a robot a high level of autonomous tasks, such as navigation, cost
mapping, object recognition, intelligent control of ground and
aerial robots, and clustering, with real-time implementations. The
reader will learn various methodologies that can be used to solve
each stage on autonomous navigation for robots, from object
recognition, clustering of obstacles, cost mapping of environments,
path planning, and vision to low level control. These methodologies
include real-life scenarios to implement a wide range of artificial
neural network architectures. Includes real-time examples for
various robotic platforms. Discusses real-time implementation for
land and aerial robots. Presents solutions for problems encountered
in autonomous navigation. Explores the mathematical preliminaries
needed to understand the proposed methodologies. Integrates
computing, communications, control, sensing, planning, and other
techniques by means of artificial neural networks for robotics.
The book offers an insight on artificial neural networks for giving
a robot a high level of autonomous tasks, such as navigation, cost
mapping, object recognition, intelligent control of ground and
aerial robots, and clustering, with real-time implementations. The
reader will learn various methodologies that can be used to solve
each stage on autonomous navigation for robots, from object
recognition, clustering of obstacles, cost mapping of environments,
path planning, and vision to low level control. These methodologies
include real-life scenarios to implement a wide range of artificial
neural network architectures. Includes real-time examples for
various robotic platforms. Discusses real-time implementation for
land and aerial robots. Presents solutions for problems encountered
in autonomous navigation. Explores the mathematical preliminaries
needed to understand the proposed methodologies. Integrates
computing, communications, control, sensing, planning, and other
techniques by means of artificial neural networks for robotics.
Bio-inspired Algorithms for Engineering builds a bridge between the
proposed bio-inspired algorithms developed in the past few decades
and their applications in real-life problems, not only in an
academic context, but also in the real world. The book proposes
novel algorithms to solve real-life, complex problems, combining
well-known bio-inspired algorithms with new concepts, including
both rigorous analyses and unique applications. It covers both
theoretical and practical methodologies, allowing readers to learn
more about the implementation of bio-inspired algorithms. This book
is a useful resource for both academic and industrial engineers
working on artificial intelligence, robotics, machine learning,
vision, classification, pattern recognition, identification and
control.
Artificial Neural Networks for Engineering Applications presents
current trends for the solution of complex engineering problems
that cannot be solved through conventional methods. The proposed
methodologies can be applied to modeling, pattern recognition,
classification, forecasting, estimation, and more. Readers will
find different methodologies to solve various problems, including
complex nonlinear systems, cellular computational networks, waste
water treatment, attack detection on cyber-physical systems,
control of UAVs, biomechanical and biomedical systems, time series
forecasting, biofuels, and more. Besides the real-time
implementations, the book contains all the theory required to use
the proposed methodologies for different applications.
Neural Networks Modelling and Control: Applications for Unknown
Nonlinear Delayed Systems in Discrete Time focuses on modeling and
control of discrete-time unknown nonlinear delayed systems under
uncertainties based on Artificial Neural Networks. First, a
Recurrent High Order Neural Network (RHONN) is used to identify
discrete-time unknown nonlinear delayed systems under
uncertainties, then a RHONN is used to design neural observers for
the same class of systems. Therefore, both neural models are used
to synthesize controllers for trajectory tracking based on two
methodologies: sliding mode control and Inverse Optimal Neural
Control. As well as considering the different neural control models
and complications that are associated with them, this book also
analyzes potential applications, prototypes and future trends.
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