def run(self):
""" inputs[0] = ERROR AXIS ., so stores all possible error values
inputs[1] = DEL_ERROR AXIS ., ,,
inputs[2] = CONTROL_OUTPUT AXIS ., ,,
ERROR DEL_ERROR CONTROL_OUTPUT m_value for crisp e and delta_e values
b[0][0] -ve Medium || b[1][0] -ve Medium || b[2][0] -ve Medium .. f[0] | f_d[0]
b[0][1] -ve small || b[1][1] -ve small || b[2][1] -ve small .. f[1] | f_d[1]
b[0][2] zero || b[1][2] zero || b[2][2] zero .. f[2] | f_d[2]
b[0][3] +ve small || b[1][3] +ve small || b[2][3] +ve small .. f[3] | f_d[3]
b[0][4] +ve Medium || b[1][4] +ve Medium || b[2][4] +_ve Medium .. f[4] | f_d[4]
f_mat is fuzzy fuzzy_matrix
"""
inputs = [ np.arange(var[0], var[1]+1, 1) for var in self.var_ranges] #step size = 1, third dimension of b matrix. As of now, an assumption.
b = []
output = [0,0,0,0,0]
out_final = []
for i in range(3) :
b.append( [membership_f(self.mu[i], inputs[i], a) for a in self.d_mu[i] ])
# To visualize the membership func. call .. [ visualize_mf(b,inputs) ]
f ,f_d = error_fuzzify(inputs, b, self.error, self.delta_e)
f_mat = fuzzy_matrix(f,f_d)
output = rule_base(b, f_mat, output)
print 'output : ', output
aggregated = np.fmax(output[0], np.fmax(output[1],np.fmax(output[2], np.fmax(output[3], output[4]))))
out_final = fuzz.defuzz(inputs[2], aggregated, 'centroid')
out_activation = fuzz.interp_membership(inputs[2], aggregated, out_final) # for plot
visualize.visualize_mf(b,inputs,output, out_final, out_activation, aggregated)
visualize.visualize_output(b, inputs, out_final, out_activation, aggregated)
plt.show()
def run(self):
"""
Finds appropriate value of pid gains
NO arguments :
inputs : List to contain discrete values of io variables in their range (step size = 1) for plotting. i.e, x axis
inputs[0] = ERROR AXIS ., so stores all possible error values
inputs[1] = DEL_ERROR AXIS ., ,,
inputs[2] = CONTROL_OUTPUT AXIS ., ,,
b : 3d list, each layer (i.e, 2d list) contains 1d lists of y-values (in x of step size 1) of a particular fuzzy
subset of a particular i/o variable
muval_de, muval_e: Stores membership value of error and delta_error for each fuzzy subsets
ERROR DEL_ERROR CONTROL_OUTPUT m_value for crisp e and delta_e values
b[0][0] -ve Medium || b[1][0] -ve Medium || b[2][0] -ve Medium .. muval[0] | muval_d[0]
b[0][1] -ve small || b[1][1] -ve small || b[2][1] -ve small .. muval[1] | muval_d[1]
b[0][2] zero || b[1][2] zero || b[2][2] zero .. muval[2] | muval_d[2]
b[0][3] +ve small || b[1][3] +ve small || b[2][3] +ve small .. muval[3] | muval_d[3]
b[0][4] +ve Medium || b[1][4] +ve Medium || b[2][4] +_ve Medium .. muval[4] | muval_d[4]
f_mat is a 2d matrix containing rule strengths
"""
inputs = [ np.arange(var[0], var[1]+1, 1) for var in self.io_ranges]
b = []
for i in range(3) :
b.append( [membership_f(self.mf_types[i], inputs[i], a) for a in self.f_ssets[i] ])
# visualize.visualize_mf(b,inputs)
# fuzzify Error and delta error to obtain their membership values for corr. fuzzy subsets
muval_e = fuzzify(inputs[0], b[0], self.error)
muval_de = fuzzify(inputs[1], b[1], self.delta_e)
# print 'muval_e:', muval_e
# print 'muval_de:', muval_de
# Obtain the rule strength matrix
f_mat = fuzzy_matrix(muval_e, muval_de)
# obtian the y value clipped by output activation for output fuzzy subsets
output = rule_base(b, f_mat)
aggregated = np.fmax(output[0], np.fmax(output[1],np.fmax(output[2], np.fmax(output[3], output[4]))))
out_final = fuzz.defuzz(inputs[2], aggregated, 'centroid')
print "output:",out_final
# plotting final output
visualize.visualize_output(b, inputs, output, out_final, aggregated)
plt.show()