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Physics - Complexity | Modeling of Complex Processes and Systems - An Integrated Approach from Measurement to Cognition

Modeling of Complex Processes and Systems

An Integrated Approach from Measurement to Cognition

Barrios, Luis J.

2015, 250 p. 50 illus.

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  • Comprehensive examination of the predictive modeling of complex processes and systems
  • Provides real-life examples and shows which situations each technique and method are best suitable for
  • Introduces key concepts and guides readers through the natural progression from data to knowledge
  • Integrated approach links concepts and methods from fields that traditionally have been separately introduced
Modeling of Complex Processes and Systems: An Integrated Approach from Measurement to Cognition, provides a broad coverage of the experimental, analytical, and computational subjects included in the modeling of complex phenomena, including fundamental principles and practical applications. The book reviews the varied causes of complication and complexity and shows the convenient approaches to the study of complex phenomena. Also presented is a structured collection of modeling methods and tools from fields like: Measurement Science, Statistics, Signal Processing, Dynamical Systems Theory, and Artificial Intelligence. The material is structured around the scientific discovery methodology: each method is introduced to reduce the degree of complexity resulting from the application of a previous one. Thus, the book offers an integrated approach for building models in complex situations, which is a helpful resource for understanding and predicting the behavior of complex processes and systems.

Content Level » Research

Keywords » Cognitive Systems - Complex systems - Dynamic Systems Theory - Dynamic systems - Inverse modeling - Machine Learning - Non-linear Dynamic Systems - Pattern recognition - computational methods - data acquisition systems - modeling of complex systems

Related subjects » Complexity - Signals & Communication - Theoretical Computer Science

Table of contents 

Introduction.- Systems theory.- Measurement.- Variability.- Design of experiments.- Digital signal processing.- Dimensionality.- Model building.- Inverse modeling.- Dynamical System.- Complexity.- Pattern recognition and machine learning.- Integrated modeling methods.- Cognitive Systems.

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