The algorithmic and software implementation of theory of optimized Bayesian dynamic advising served as a basis for construction of advisory system intended to support the decision-maker.
To customise a particular advisory system, a large sample of historical data taken from managed process is analysed and processed offline. The obtained results are complemented by information about the expected advisory levels and decision-making aims.
A core of the advisory system forms Mixtools package, which has been implemented both: as a toolbox within MATLAB environment and as MATLAB-independent code. The MATLAB-like implementation is intended to serve to research and simulation purposes. Another implementation can be integrated with an existing control and/or monitoring system of the process managed and, thus, can serve to real-time, full-scale application.
The advisory system was implemented and extensively tested on several different case studies: prediction of urban traffic, treatment of thyroid gland carcinoma and fault detection and isolation problem. A real-time, full-scale industrial implementation of the advisory system on cold rolling mills confirmed the generic nature of the tool and illustrated the following key features of the solution:
The system and its core Mixtools package are permanently innovated and improved. For the latest version, please, contact L. Tesař.
The toolbox has been developed under support of the following grants:
http://mys.utia.cas.cz:1800/svn/mixtools