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The ring strengths of rules represent the degree of membership to the sets in the consequent of the corresponding rule Given a set of activated rules and their corresponding ring strengths, the task of the defuzzi cation process is to convert the output of the fuzzy rules into a scalar, or non-fuzzy value For the sake of the argument, suppose the following hedges are de ned for linguistic variable C (refer to Figure 213(a) for the de nition of the membership functions): large decrease (LD), slight increase (SI), no change (NC), slight increase (SI), and large increase (LI) Assume three rules with the following C membership values: LI = 08, SI = 06 and N C = 03 Several inference methods exist to nd an approximate scalar value to represent the action to be taken: The max-min method: The rule with the largest ring strength is selected, and it is determined which consequent membership function is activated The centroid of the area under that function is calculated and the horizontal coordinate of that centroid is taken as the output of the controller For our example, the largest ring strength is 08, which corresponds to the large increase membership function Figure 213(b) illustrates the calculation of the output The averaging method: For this approach, the average rule ring strength is calculated, and each membership function is clipped at the average The centroid of the composite area is calculated and its horizontal coordinate is used as output of the controller All rules therefore play a role in determining the action of the controller Refer to Figure 213(c) for an illustration of the averaging method The root-sum-square method: Each membership function is scaled such that the peak of the function is equal to the maximum ring strength that corresponds to that function The centroid of the composite area under the scaled functions are computed and its horizontal coordinate is taken as output (refer to Figure 213(d)) The clipped center of gravity method: For this approach, each membership function is clipped at the corresponding rule ring strengths The centroid of the composite area is calculated and the horizontal coordinate is used as the output of the controller This approach to centroid calculation is illustrated in Figure 213(e) The calculation of the centroid of the trapezoidal areas depends on whether the domain of the functions is discrete or continuous For a discrete domain of a nite number has its of values, nx , the output of the defuzzi cation process is calculated as ( algebraic meaning) nx xi C (xi ) (2111) output = i=1 nx i=1 C (xi ) In the case of a continuous domain ( has its algebraic meaning), output =.

//same value, so it cannot go left or //right, only up and down on(press){ startDrag("",false,left,top,right, bottom); } on(release){ stopDrag(); //The volume is a number between //zero and 100 and //represents the difference //between the bottom of //the slider and the current //position of the button //times the ratio of the full //volume (100) to the //height of the slider bar //This gives a value of //100 when the slider button //is at the top of the //slider bar, and adjusts the //volume correctly for //every position in between //the top and the bottom _rootthisSoundsetVolume((myY_y)*100/_rootmySliderBar_height); }

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Walk around the location Watch the action from potential camera positions As you move around, pick the parts of the event your audience will find the most interesting As you experiment with different POVs, look for high and low angles, framing possibilities, annoying backgrounds, clashing colors, bright lights, and so on Use your eyes, not the camera, to find your best angles If you use the viewfinder to search out shots, you will drain your camera s battery and miss potentially exciting footage You don t have to accept the location as you find it Don t be afraid to ask permission to move furniture, objects, or people if the change gives better results Most video production participants eagerly cooperate if you tell them that their efforts will add quality to the project

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Another variant of the Hebbian learning rule uses the correlation in the changes in activation values over consecutive time steps For this learning rule, referred to as differential Hebbian learning, uki (t) = zi (t) ok (t 1) where zi (t) = zi,p (t) zi,p (t 1) and ok (t 1) = ok,p (t 1) ok,p (t 2) (411) (410) (49)

Select Frame 2 in the other layers and add a frame (F5)

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