An Interdisciplinary Journal for Advanced Science and Technology
"Evolving Systems" covers surveys, methodological, and application-oriented papers in the emerging area of evolving systems. Evolving systems are inspired by the idea of system model evolution in a dynamically changing and evolving environment. They use inheritance and gradual change with the aim of life-long learning and adaptation, self-organization including system structure evolution in order to adapt to the (unknown and unpredictable) environment as structures for information representation with the ability to fully adapt their structure and adjust their parameters.
"Evolving Systems" solicits publications that address the problems of modelling, control, prediction, classification and data processing in non-stationary, unpredictable environments and describe new methods and approaches for design of systems able to fully adapt its structure rather than adjust its parameters based on a pre-trained and fixed structure.
The journal is devoted to the topic of self-developing, self-organised, and evolving systems in its entirety - from systematic methods to case studies and real industrial applications. It covers all aspects of the methodology such as
- conventional systems,
- neuro-fuzzy systems,
- evolutionary systems,
- Bayesian systems,
- machine learning methods,
- clustering, and
but also looking at new paradigms and applications, including medicine, robotics, business, industrial automation, control systems, transportation, communications, environmental monitoring, biomedical systems, security, and electronic services. The common features for all submitted methods and systems are evolvability and knowledge discovery.
The journal is encompassing contributions related to:
1) Methods of computational intelligence and mathematical modelling
2) Inspiration from Nature and Biology, including Neuroscience, Bioinformatics and Molecular biology, Quantum physics
3) Applications in engineering, business, social sciences.
Stefan Marks (September 2017)
Design of spiking neural networks for blood pressure prediction during general anesthesia: considerations for optimizing results
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