Programming rule-based

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. 

One variation of rule-based systems are fuzzy logic systems. These programs use statistical decision-making processes in which they can account for the fact that a specific piece of data has a certain chance of indicating a particular result. All these probabilities are combined in order predict a final answer. 

This is a recursion formula for the exact case. We would like to be able to apply this to any number n of CSTRs in series and find an analytical and then quantitative result for comparison to the exact PFR result. To do this weneedrecursive programming. There are threeprogrammingstylesin Mathematica Rule-Based,Functional,and Procedural.Wewill attackthisprobleminrecursionwith Rule-Based,Functional,and Procedural programming. WecanbeginbylookingattherM/e-tosed recursioncodesforCaandCbinanynCSTRs. 

The common method of treating rod-pumped wells is to periodically batch inhibitor into them. The treatment period for a given well is selected using empirical rules based on well production volumes. A successful and economic corrosion inhibition program must carefully control the inhibitor concentration in the well fluids. Environmental aspects and efficacious inhibitor usage necessitate the measurement of very low corrosion inhibitor concentrations. Inhibitor concentrations as low as one part per million are significant, thus... 

Similar to attributes, procedures can be inherited by child frames when inheritance is supported. Object-oriented programming enormously enlarges the flexibility of frame-based systems. A disadvantage is, however, that it results often in unclear and intractable reasoning structures. Table 43.2 shows the small rule base of Fig. 43.2, translated in an object-oriented system. 

In this case, systematic methods for generation of feasible molecular and mixture candidates have been reported for the design of functional chemical products [see Achenie et al (2003)]. Methods based on database search, total enumeration of feasible candidates (rule-based techniques that avoid a combinatorial explosion), mathematical programming, genetic algorithm, and,... 

The logical deduction portion of the program is based on IF-THEN rules. FACTS, acquired both as the result of logical deductions and by querying the user, are stored in similar data structures. Because the branch points in the problem are also logical deductions, they are stored in a data structure similar to the FACTS. The branch points contain additional flow of control information that relates to the hierarchy of the problem. The difference between FACTS and branch points is transparent to the logical deduction portion of the program. 

Buchanan,B.A., Shortliffe, E.H. "Rule Based Expert Programs The MYCIN Experiments of the Stanford Heuristic... 

The current protot3rpe system includes three Expert modules, the IR Expert, the STIRS Expert, and the Human. All modules are written in Lisp. The IR Expert is a rule-based infrared interpreter which we have developed. The STIRS Expert is an interface to the STIRS program, a pattern-matching mass spectrum interpreter developed by McLafferty and coworkers at Cornell University, which is written in Fortran. () ) The interface translates the output of STIRS into a form palatable to our program, and handles the message-passing protocol required by the Controller. The Human module controls communication with the user. It allows user-supplied elemental or substructure information to influence the course of the analysis. The power of... 

A rule-based infrared spectra interpreter has been developed as a major module of the program. This module has been tested as a stand-alone system, and in conjunction with STIRS. The low rate of false positive assertions is encouraging, and work continues to reduce this rate still further by incremental refinement of the knowledge base. 

To find systematically the right solvents for TMS systems it appeared desirable to establish a decision tree for solvent selection [41], Subsequently this tree was implemented into an expert system program [42], This program is based on the miscibility rules of Berg [43] who classified the solvents into five groups (Table 13). 

Expert systems are easy to program and to understand because they usually resemble instructions in English. The time and cost for developing these systems is relatively small. The primary problem usually turns out to be interpreting the sensors. Because first and second derivatives of sensor data are used to find trends and patterns, noise can be a major problem. The rules allow the controller to adapt to the condition of the material and to the geometry of the part. Expert systems make it relatively easy to change to backup plans when sensor or equipment failures occur. In fact, rule-based systems can be quite general and handle a number of materials with little material specific data. 

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