# Define the following fuzzy rules:
# 1 If E is negative and CE is negative, then u is -20.
# 2 If E is negative and CE is positive, then u is 0.
# 3 If E is positive and CE is negative, then u is 0.
# 4 If E is positive and CE is positive, then u is 20.
ruleList = [
[0, 0, 0, 1, 1], # Rule 1
[0, 1, 1, 1, 1], # Rule 2
[1, 0, 1, 1, 1], # Rule 3
[1, 1, 2, 1, 1]
] # Rule 4
fis.addRule(ruleList)
Step = 1
E = linspace(-10, Step, 10)
CE = linspace(-10, Step, 10)
N = len(E)
LookUpTableData = np.zeros((N, N))
for i in range(N):
for j in range(N):
# Compute output u for each combination of sample points.
LookUpTableData[i, j] = evalfis(fis, [E[i], CE[j]])
print(LookUpTableData)
from matplotlib.pyplot import plot, xlabel, show
from fuzzylab import linspace, gaussmf2
x = linspace(0, 0.1, 10)
y1,y2 = gaussmf2(x, [1,2, 5])
plot(x,y1)
plot(x,y2)
xlabel('gaussmf2, P=[2, 5]')
show()
import numpy as np from matplotlib.pyplot import plot, ylim, show, vlines, text from fuzzylab import linspace, trapmf, defuzz x = linspace(-10, 0.1, 10) mf1 = trapmf(x, [-10, -8, -2, 2]) mf2 = trapmf(x, [-5, -3, 2, 4]) mf3 = trapmf(x, [2, 3, 8, 9]) mf1 = np.fmax(0.5 * mf2, np.fmax(0.9 * mf1, 0.1 * mf3)) x1 = defuzz(x, mf1, 'centroid') x2 = defuzz(x, mf1, 'bisector') x3 = defuzz(x, mf1, 'mom') x4 = defuzz(x, mf1, 'som') x5 = defuzz(x, mf1, 'lom') gray = [x * 0.7 for x in [1, 1, 1]] vlines(x1, -0.2, 1, color='r') text(x1, -0.24, ' centroid', fontweight='bold', color='r') vlines(x2, -0.4, 1, color=gray) text(x2, -0.44, ' bisector', fontweight='bold', color=gray) vlines(x3, -0.7, 1, color=gray) text(x3, -0.74, ' MOM', fontweight='bold', color=gray) vlines(x4, -0.8, 1, color=gray) text(x4, -0.84, ' SOM', fontweight='bold', color=gray) vlines(x5, -0.6, 1, color=gray) text(x5, -0.64, ' LOM', fontweight='bold', color=gray) plot(x, mf1, lineWidth=3)