The wide and increasing needs of adaptable and flexible solutions for many industrial, environmental, and engineering applications point out the importance of using design methodologies and implementation technologies with high ability of adaptation and evolution. Soft computing is one of the most relevant answers to such needs: neural networks, fuzzy logic, and genetic/evolutionary algorithms are fundamental keys to tackle these difficult problems. On the other hand, accuracy and uncertainty issues as well as suited data acquisition systems must be carefully considered in these applications since the quality of the solution greatly relies on them. Up to now, analysis and experiments have been performed by scientists and practitioners mainly to understand the underlying technologies and methodologies, but without any specific focus on the mandatory need of a quantitative assessment and a metrological analysis. Measurement science and technologies are vital to ensure the correct and effective use of soft computing technologies in real environments. CIMSA 2012 will continue the exciting experience of the previous editions by filling this gap in knowledge and practice, by focusing on the quantitative aspect of measurement issues for industrial, environmental, and engineering applications.
Papers are solicited on all aspects of computational intelligence technologies for measurement systems and the related applications, from the points of view of both theory and practice. This includes but is not limited to: intelligent measurement systems; accuracy and precision of neural and fuzzy components; intelligent sensor fusion; intelligent monitoring and control systems; neural and fuzzy technologies for identification, prediction, and control of complex dynamic systems; evolutionary monitoring and control; neural and fuzzy signal/image processing for industrial and environmental applications; image understanding and recognition; soft-computing technologies for robotics and vision; soft computing technologies for medical and bioengineering applications; hybrid systems; fuzzy and neural components for embedded systems; hardware implementation of neural and fuzzy systems for measurements; neural, fuzzy and genetic/evolutionary algorithms for system optimization and calibration; neural and fuzzy diagnosis of components and systems; reliability of fuzzy and neural components; fault tolerance and testing in fuzzy and neural components; neural and fuzzy techniques for quality measurement.