Computational Intelligence by Diego Andina, Duc Truong Pham PDF

By Diego Andina, Duc Truong Pham

Computational Intelligence is tolerant of obscure details, partial fact and uncertainty. This publication offers a specific number of contributions on a targeted remedy of vital parts of CI, concentrated on its key point: studying. This publication offers novel purposes and actual international purposes operating in production and Engineering, and it units a foundation for figuring out Domotic and construction tools of the XXI Century.

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The updating is implemented via a rule induction program. The program employs RULES-3 Plus which automatically extracts new feature recognition rules from examples provided to it in the form of characteristic vectors representing different features and their respective class labels. Rule induction is very suitable for this application because of the complexity of the characteristic vectors and the difficulty of defining feature classes manually. Figure 13a. An assembly model SOFT COMPUTING AND ITS APPLICATIONS IN ENGINEERING AND MANUFACTURE 29 Bolt: • Child of Block • Placement constraints: 1: alignment of two axes 2: mating ofthe bottom surface of the bolt head and the upper surface ofthe block • No child part in the assembly hierarchy Block: • No parents • No constraints (root component) • Next part in the assembly: Bolt Figure 13b.

In practice, the frequency measurements have low repeatability, which leads to inconsistent location information. The problem worsens when is in the region 80 -90 relative to a reference axis of the sensor because the mathematical model becomes ill-conditioned. In this “ill-conditioning” region, an alternative to using a mathematical model to compute is to adopt an experimentally derived fuzzy model. 7 St. 5 45 60 St. 0 State of the process: in-control Warning !!!!!! Process going out of control!

To counteract the effects of the poor repeatability of period measurements which are particularly noticeable in the “ill-conditioning” region, the fuzzy rules are modified so that they take into account the variance in T. An example of a modified fuzzy rule is: “IF T-Tref is small and T is small, THEN − ref is small ” In the above rule, T denotes the standard deviation in the measurement of T. Fuzzy modelling of the vibratory sensor is detailed in Pham and Hafeez (1992). Using a fuzzy model, the orientation can be determined to ±2 accuracy in the region 80 -90 .

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