Research Topic

Network System Modeling: real applications

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Today, bio-inspired approaches that bring together biology, engineering, computing and physics are driving new developments in terms of novel approaches and applications in engineering. For example, the use of complex network theory for modeling neural activity or development of new dynamic systems for the ...

Today, bio-inspired approaches that bring together biology, engineering, computing and physics are driving new developments in terms of novel approaches and applications in engineering. For example, the use of complex network theory for modeling neural activity or development of new dynamic systems for the control of prostheses and exoskeletons have been developed. The use of a dynamic systems approach to model nervous system function as an information processing system could lead to the development of new controllers, signal processing algorithms and computer architecture. Understanding how signals are processed serves as a centerpiece for modeling bio-inspired systems.

In the search for better models to explain real-world processes it often becomes necessary to use probabilistic reasoning. Many complex systems impose several limitations of observability and in terms of mathematical description their variables are described as random, and their internal processes as stochastic. The search for dependency relationships among these processes points to the concept of causality that allows a deeper insight into a systems function and constituents parts. Moreover, the general idea of system predictability allows a better understanding of system behaviour in advance of its occurrence by means of specialized knowledge or inference. The limited predictability of a phenomenon underlying the behaviour of a system leads to the conclusion that one cannot anticipate the full list of complex behaviours. In this case, more sophisticated models based on stochastic intense algorithms such as network systems, layered collaborative systems, or Bayesian models will drive future developments with many applications in Biomedical Engineering. The construction of these complex networks poses several challenges. A simple observation of the data is not sufficient to propose a network model so that it must be accomplished by the use of optimization techniques and machine learning to generate models of best fit.

The Research Topic on Network System Modelling will be a forum to discuss the latest advances in algorithms for large system modelling and applications in Biomedical Engineering that are currently state of the art. The topic would then include (but not be limited to) studies involving linear and nonlinear dynamic system modelling, model performance, and its applications to real data.


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