Rule-based optimization

An example of the cure cycle optimization is the work of Thomas et al. who used a very sophisticated model, together with a rule-based optimization routine, to pick the shortest cure cycle that met a set of performance criteria [15]. Reductions in cure time using this method ranged up to 36 percent for a single complex part and from 8 percent up to 43 percent for batches of mixed parts. Rejection rates were not increased in any case, and they were actually reduced significantly for one part. This model, although transferred to a number of companies, unfortunately has had limited use because of the lack of support for the code and the cost of qualifying it on new materials. 

A geometric approach has been adopted to optimize the rule base, such that ittakes fewer variables for rule base optimization. In this approach we keep the symmetry in the rule base as shown in Table 2 about the premise [0 0] intact. The following assumptions consistent with structural control design are made while designing the rule base. 

From the different planning methods available within SNP, SNP optimization is selected because it offers the best fit to the customer requirements outlined above. The main reasons for this decision are the multisourcing characteristics of the supply network as well as the fact that the objective functions used by the SNP optimizer, profit maximization or cost minimization, correspond to the planning philosophy favored by the customer. In addition to SNP optimization with its cost-based approach, SNP offers several heuristic-based planning methods which follow a rule-based logic. 

ARC is not an expert system in the classical sense it does not provide ques-tion-and-answer strategies or a generic rule base. It includes nonlinear optimization algorithms and guides the user through the necessary steps for creation and evaluation of descriptors. 

As a rule, chromatographic optimizations are based on the trial-and-error approach, relying on experimentation, and the basic relationships of chromatographic theory. In HPLC [58-62] is used the well-known expression for the resolution... 

Here, the premise is described by a membership function for the linguistic variable high and the function for the detection limit is the sum of the blank signal, y, and three times the standard deviation of the blank signal, Sg, (cf. Eq. (4.3)). Optimization of the parameters in the premise part of the rules is adaptively done by combining the fuzzy rule-based system with a neural network. Consider an adaptive neuro-fuzzy system with two inputs, and... 

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