System expert

An expert system is a computer program to reproduce a problem-solving manner according to an expert s knowledge and inference procedures in a computer system. Expert systems have been used for fault diagnosis in bioprocess operations, the improvement of medium composition, and the optimization of culture conditions, since it can handle a large amount of information and data concerning bioprocess operation and optimization [13]. 

1 Summarize the difference between bioprocesses and chemical processes in terms of process control. 

2 Give the operating principle of the following sensors in a few sentences. 

3 Explain the reason why a proportional control (P action) results in the off-set between the set-point and the measured process variable at a steady state. 

4 Determine the parameters, K, T, and Tp, in PID controller by the ultimate gain method when the ultimate gain and the oscillation cycle time, T, are 4.0 and 30 s, respectively. 

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Roles for an expert system can be learned by rule induction from a set of examples. This makes this method similar to the use of classifiers - it will solve problems of similar complexity and have similar disadvantages (e.g. possibility of unnoticed misclassifications). 

Expert systems. In situations where the statistical classifiers cannot be used, because of the complexity or inhomogeneity of the data, rule-based expert systems can sometimes be a solution. The complex images can be more readily described by rules than represented as simple feature vectors. Rules can be devised which cope with inhomogeneous data by, for example, triggering some specialised data-processing algorithms. 

Construction of expert systems is facilitated if it is possible (at least approximately) to describe (model) expected signal from defect and non-defect pieces. If no models for the problem are available then the knowledge about the problem has to be acquired from an expert (the NDT inspector). However, the knowledge possessed by the expert is often incomplete and not well formalised, which makes knowledge acquisition a difficult task for the knowledge engineer. 

The recognition ratios achieved by CBR systems developed as part of this project could not be bettered by either neural-network classifiers or rule-based expert system classifiers. In addition, CBR systems should be mote reliable than simple classifiers as they are programmed to recognise unknown data. The knowledge acquisition necessary to build CBR systems is less expensive than for expert systems, because it is simpler to describe the knowledge how to distinguish between certain types of data than to describe the whole data contents. 

Gianatano, J. and Riley, G., (1989), Expert Systems Principles and Programming, PWS-KENT Publishing Company, Boston, 1989. 

At the start of the development, it had been intended use an expert system shell to implement this tool, however, after careful consideration, it was concluded that this was not the optimum strategy. An examination procedure can be considered as consisting of two parts fixed documentary information and variable parameters. For the fixed documentary information, a hypertext-like browser can be incorporated to provide point-and-click navigation through the standard. For the variable parameters, such as probe scanning paths, the decisions involved are too complex to be easily specified in a set of rules. Therefore a software module was developed to perfonn calculations on 3D geometric models, created fi om templates scaled by the user. 

To appreciate the architecture and tasks of expert systems and examples of expert systems in chemistry... 

It extends the usage of statistical methods and combines it with machine learning methods and the application of expert systems. The visualization of the results of data mining is an important task as it facilitates an interpretation of the results. Figure 9-32 plots the different disciplines which contribute to data mining. 

Machine Learning ------M Data Mining [<-------- Expert Systems... 

The classical architecture of an expert system comprises a knowledge base, an inference engine, and some kind of user interface. Most expert systems also include an explanation subsystem and a knowledge acquisition subsystem. This architecture is given in Figure 9-34 and described in more detail below. 

LJsttr Interface All expert systems have some kind of user interface which provides the user with a means of interacting with the system. 

Besides these three major components, many expert systems also comprise an explanation subsystem and a knowledge acquisition subsystem,... 

Many expert systems also apply fnt zy logic (see Section 9.6) instead of strict Boolean logic and arc thus able to cope with rules which arc imprecise or incomplete. 

Expert systems have been applied in various fields such as medicine, chemistiy, engineering, or business. Within all these fields, expert systems are used for similar types of tasks. They can, for example, be used for classification, i.c, to select one... 

Expert systems are widely applied in design and planning where they can give instructions on how to design systems for given criteria or constraints. Expert systems are also used for simulation tasks Starting from an initial state, these systems deduce subsequent states with the aim of simulating possible outcomes for certain actions. 

In chemistry and chemical engineering, expert systems are used for various tasks ranging from laboratory automation or reaction kinetics to the design of syntheses or the simulations of processes [24]. The application of expert systems in chemistry is described in more detail in Chapter IX, Section 2 of the Handbook,... 

Two systems will be introduced below on the one hand the DENDRAL system for automatic structure elucidation [25], which was one of the first expert systems on the other hand the EROS system [26], which can be used for simulating reactions. 

P. Jackson, Introduction to Expert Systems, Addison Wesley Longman, Harlow, 1999. 

G. Schembecker, Chemical Engineering Expert Systems, in Encyclopedia cf Computational Chemistry, P.v.R. Schleyer, N. L. AlUnger, T. Clark,... 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

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