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Data Fusion - Detailled description
This approach was proposed in 1986 and, since 1991, it has been involved in many applications where we want to know how to use any model and how to use limited data …
In engineering practice, we are faced with problems in the design, testing, inspection, and maintenance of the products. Usually these problems are very complex and the knowledge of issues involved (inputs) is not complete. Thus, the problems are not fully understood. Moreover, the available data may be not be statistically representative (i.e. be in limited number), fuzzy, qualitative and missing in part.
The Intelligent Optimal Design of Complex Systems takes the actual best knowledges of the researchers/experts and mixes them intelligently with the results of experiments or real returns.
It was successfully applied to many real industrial systems showing explicitly the multi-level modelling and multi-disciplinary optimal design
Building a examples database
obtaining some experimental, real or simulated results where the EXPERTS indicate all variables or descriptors that may take a part.
This is, at first, done with some PRIMITIVE descriptors x, which are usually in a different number and with a different meaning for each example. Then, the data are transformed with the introduction of some INTELLIGENT descriptors XX, with the actual whole knowledge thanks to beautiful (but often insufficient) theories and models.
These descriptors may be numbers, Boolean, strings, names of files which give access to data bases, or treatments of curves, signals and images. But for all examples, their number and their type are now always the same. This is the proposed one way to allow the fusion of data coming from various sources and to reduce the number of the parameters and thus to use the limited data.
The results or conclusions may be classes (good, not good...) or numbers and guided by the shapes of the rules or relationships. | 
