PAIRS expert system

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. 

In a conventional expert system, the only rules to fire are those for which the condition is met. In a fuzzy system, all of the rules fire because all are expressed in terms of membership, not the Boolean values of true and false. Some rules may involve membership values only of zero, so have no effect, but they must still be inspected. Implicitly, we assume an or between every pair of rules, so the whole rule base is... 

The decision for each example is expressed as an "action-next state" pair. The "action" is a reference to executable Radial code, which consists of a sequence of Radial statements. These statements may contain references to external programs in various languages (this will be discussed further later). The "next state" describes the context to which control is to pass after the action is completed. For diagnostic expert systems, such as TOGA, the next state will usually be the "goal" state of the module. This passes control back to the calling module. For procedural expert systems, such as robotics and instrumentation control applications, the control will be transferred between several states within a module to Implement looping. 

Since its introduction to the scientific community in late 1980, PAIRS (Program for the Analysis of IR Spectra) has been used successfully by a large number of researchers. Recent improvements to PAIRS have made this package incorporate most of the aspects of expert systems. The improvement highlighted in this paper is the capability for scientists to inquire of the system why a particular interpretation result was achieved. This capability enhances the ability for scientists to learn from the knowledge base of interpretation rules present in PAIRS. It also simplifies the process by which the PAIRS knowledge base can be refined through incorporation of improved rules from expert spectroscopists. 

Production rules are a formalism commonly used in expert systems but which have their origin in automata theory and formal grammars. Each rule consists of an antecedent-consequent pair, for example ... 

First, for fashion pair C, (1 < i < ), ntilize the rule-based expert system to evaluate the attribute matching satisfaction index, and obtain a collection of m satisfaction degree index corresponding to m evalnation criteria, that is ... 

We divided the 829 pairs into seven groups (rated using a seven-level scale) on the basis of their FMSI prodnced by the program. Table 10.4 indicates the results of the mix-and-match recommendations generated by the IPCS system. The number of pairs in the P Perfect) level was zero, which means that the matching rules in the expert system for perfect are veiy hard to meet. The numbers of pairs in the levels of LB, B and VB were 171 (20.63%), 303 (36.55%) and 205 (24.73%) respectively. 

Production rules, also known as if-then pairs or condition-action pairs, form the basis of rule-based expert systems. They resemble implication statements in predicate calculus and can not only generate new facts, but can also run other programs, activate or deactivate other rule-sets, and even change the... 

These inspections can be either informal or formal. The informal approach is to have a complete in-depth review of the entire process with a written report. The formal approach is to have a mock FDA inspection, which is extremely useful in identifying system and process failures at all levels. The latter offers practice in both preparing for and interacting with an FDA investigator as well as testing the breadth and depth of documentation that would be needed for the FDA inspection process. In all cases, the use of a third-party experts allows for a more objective evaluation of the entire process from a fresh pair of eyes and from someone who can offer an objective comparison against current industry standards. 

Zone sampling (see 7.2) can be exploited in some expert flow systems with LLE in order to improve their performance, as initially demonstrated by the determination of Ci—C5 n—mercaptans in gasoline [199] (see also 8.7). Another example is the determination of ammonium by exploiting ion pairing [200]. 

In several instances, Mannich-type cyclizations can be carried out expeditiously under photochemical conditions. The photochemistry of iminium ions is dominated by pathways in which the excited state im-inium ion serves as a one-electron acceptor. The photophysical and photochemical ramifications of such single-electron transfer (SET) processes as applied to excited state iminium ions have been expertly reviewed. In short, one-electron transfer to excited state iminium ions occurs rapidly from one of several electron donors electron rich alkenes, aromatic hydrocarbons, alcohols and ethers. Alternatively, an excited state donor, usually aromatic, can transfer an electron to a ground state iminium ion to afford the same reactive intermediates. Scheme 46 adumbrates the two pathways that have found most application in intramolecular cyclizations. Simple alkenes and aromatic hydrocarbons will typically suffer addition processes (pathway A). However, alkenic and aromatic systems with allylic or benzylic groups more electrofugal than hydrogen e.g. silicon, tin) commonly undergo elimination reactions (pathway B) to generate the reactive radical pair. 

To validate the performance of the proposed IPCS system on matching recommendations for cross-selling in a real-life environment, 48 fashion items of a fashion retailer, including a total of 538 expert rules, were used for experimental testing. These samples of fashion items belong to five product types, including dress, jacket, skirt, top and trousers. The distribution of apparel items in each product type and the potential 829 matching pairs are listed in Table 10.3. 

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