Logic-based knowledge representation

The fundamental idea is to utilize additional relevant process information for assessing the correctness of information generated by a sensor. This approach is known as the functional redundancy and it is more attractive than physical redundancy by duplicating sensors and using a voting logic to select the correct information. Several techniques based on statistics and system theory have been developed for validation of sensor information by functional redundancy. In most of these techniques, it is assumed that detailed process information is available a priori. Often, this knowledge is in the form of an accurate state-space model [39, 230]. In many cases, this type of accurate representation of a chemical process based on first principles is not available. 

The general principle in fuzzy logic is that a reference value Xq is associated with a fuzzy interval dx, and experimental data within an interval of Xq dx are identified as reference data. Since natural, or experimental, data are always inaccurate, and the representation of knowledge is quite like that in fuzzy logic, expert systems have to use fuzzy logic or some techniques similar to fuzzy logic [33]. In a computer system based on the fuzzy logic approach, fuzzy intervals for reference values are defined a priori. 

A model may be defined as the simplified repn sentatioH of a defined physical system. The representation is developed in symbolic form and is frequently expressed in mathematical equations and uses physical and or chemical principles based on scientific knowledge, experimental judgment, and intuition, all set on a logical foundation. A model may be theoretical or empirical, but the formulation of an accurate model is a requirement for the successful solution of any problem. 

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