Logica difuza

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This is the fourth in a series of six articles intended to share information and experience in the realm of fuzzy logic (FL) and its application. This article will continue the example from Part 3 by introducing membership functions and explaining how they work. The next two articles will examine FL inference and defuzzification processes and how they work. For further information,several general references are included at the end of this article.
In the last article, the rule matrix was introduced and used. The next logical question is how to apply the rules. This leads into the next concept, the membership function.
The membership function is a graphical representation of the magnitude of participation of each input. It associates a weighting witheach of the inputs that are processed, define functional overlap between inputs, and ultimately determines an output response. The rules use the input membership values as weighting factors to determine their influence on the fuzzy output sets of the final output conclusion. Once the functions are inferred, scaled, and combined, they are defuzzified into a crisp output which drives the system.There are different membership functions associated with each input and output response. Some features to note are:
SHAPE - triangular is common, but bell, trapezoidal, haversine and, exponential have been used. More complex functions are possible but require greater computing overhead to implement.. HEIGHT or magnitude (usually normalized to 1) WIDTH (of the base of function), SHOULDERING (locksheight at maximum if an outer function. Shouldered functions evaluate as 1.0 past their center) CENTER points (center of the member function shape) OVERLAP (N&Z, Z&P, typically about 50% of width but can be less).
Figure 5 - The features of a membership function
Figure 5 illustrates the features of the triangular membership function which is used in this example because of itsmathematical simplicity. Other shapes can be used but the triangular shape lends itself to this illustration.
The degree of membership (DOM) is determined by plugging the selected input parameter (error or error-dot) into the horizontal axis and projecting vertically to the upper boundary of the membership function(s).
Figure 6 - A sample case
In Figure 6, consideran "error" of -1.0 and an "error-dot" of +2.5. These particular input conditions indicate that the feedback has exceeded the command and is still increasing.
The degree of membership for an "error" of -1.0 projects up to the middle of the overlapping part of the "negative" and "zero" function so the result is "negative" membership = 0.5 and"zero" membership = 0.5. Only rules associated with "negative" & "zero" error will actually apply to the output response. This selects only the left and middle columns of the rule matrix.
For an "error-dot" of +2.5, a "zero" and "positive" membership of 0.5 is indicated. This selects the middle and bottom rows of the rule matrix. By overlaying the two regions of the rule matrix, it can be seenthat only the rules in the 2-by-2 square in the lower left corner (rules 4,5,7,8) of the rules matrix will generate non-zero output conclusions. The others have a zero weighting due to the logical AND in the rules.
There is a unique membership function associated with each input parameter. The membership functions associate a weighting factor with values of each input and theeffective rules. These weighting factors determine the degree of influence or degree of membership (DOM) each active rule has. By computing the logical product of the membership weights for each active rule, a set of fuzzy output response magnitudes are produced. All that remains is to combine and defuzzify these output responses.
This is the fifth in a series of six articles...
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