In many human situations there are parameters that can be measured and are associated with choice of an outcome, but there is no definable 'equation'. Rather we can gather data from previous cases and use these to classify new cases.

Suppose a company business is based on providing car insurance. Driver ages are 18 to 120, which we could divide into ranges -

driver.is.young ( 18, 25) - driver.is.middle ( 25, 70) - driver.is.old ( 70+)

Similarly, car top speed is relevant.

Top speed of the car is likely to be relevant- car top speed ( <120), ( <140) ( <170)

Combinations of these amount to 'rules'. But the boundaries are not sharp, but graduated. This is the 'fuzziness'

If driver is young OR old, and car is fast, then risk is high

If driver is middle-aged, OR car is average-speed, then risk is middle

If car is slow then risk is low except for young drivers

' *** Fuzzy logic example- bluatigro+jhf **** WindowWidth = 1000 WindowHeight = 900 nomainwin open "Fuzzy insurance.inc" for graphics_nf_nsb as #m #m "trapclose quit" #m "fill lightgray" #m "size 4" for age =18 to 120 driver.is.young$ = fuz$( 0, 0, 18, 25) driver.is.middle$ = fuz$( 18, 25, 50, 70) driver.is.old$ = fuz$( 50, 70, 120, 120) for vel =0 to 300 step 2 car.is.slow$ = fuz$( 0, 0, 120, 140) car.is.average$ = fuz$( 120, 140, 170, 200) car.is.fast$ = fuz$( 170, 200, 400, 400) driverYoung = fuzz( age, driver.is.young$) driverMiddle = fuzz( age, driver.is.middle$) driverOld = fuzz( age, driver.is.old$) carSlow = fuzz( vel, car.is.slow$) carAverage = fuzz( vel, car.is.average$) carFast = fuzz( vel, car.is.fast$) ''if driver is young OR old, and car is fast, then risk is high riskHigh = min( carFast, max( driverYoung, driverOld)) ''if driver is middle-aged, OR car is average-speed, then risk is middle riskMiddle = max( driverMiddle, carAverage) ''if car is slow then risk is low except for young drivers riskLow = min( carSlow, 1 -driverYoung) ''defuzzyfication pay.is.low$ =fuz$( 0, 1, 20, 40) pay.is.middle$ =fuz$( 20, 40, 60, 80) pay.is.high$ =fuz$( 60, 80, 99, 100) ml =mass( riskLow, pay.is.low$) mm =mass( riskMiddle, pay.is.middle$) mh =mass( riskHigh, pay.is.high$) pl =point( pay.is.low$) pm =point( pay.is.middle$) ph =point( pay.is.high$) pay = ( ml *pl +mm *pm +mh *ph) /( ml +mm+ mh +1E-10) call plot vel, age, pay scan next vel next age #m "flush" wait sub plot x, y, z r = z * 2.5 if not( x mod 10) or not( y mod 10) then g = 255 b = 255 - r 'z * 2.5 #m "color "; r ;" "; g; " "; b #m "down" #m "line "; 100 +2 *y +2 *x; " "; 400 +y -x /4; " "; 100+ 2 *y +2*x; " "; 400 +y -x /4 -z #m "up" end sub function fuz$( a, b, c, d) ''construct a fuzzy-object$ fuz$ =a; " "; b; " "; c; " "; d end function function fuzz( x, f$) ''what is membervalue of x in fuzzy-object f$ a =val( word$( f$, 1)) b =val( word$( f$, 2)) c =val( word$( f$, 3)) d =val( word$( f$, 4)) uit =0 if ( b <=x) and ( x <=c) then uit =1 if ( a