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: