关于python结果的函数不一致
关于python结果的函数不一致
提问于 2022-11-22 16:42:25
我正在研究一个包含12个参数和12个方程的非线性方程组，我从FSolve开始，但是由于应用程序和计算机之间的结果不同，我继续讨论Gekko…。但问题依然存在。
我有一个例程，相同的输入数据从一个excel，运行到同一台机器(与木星§笔记本，或蜘蛛或应用程序编译的Visual，每个程序产生不同的输出！同一个木星笔记本相同的输入，但在不同的PC上，结果也不同？有人有同样的经历吗？我怎么才能解决这个问题？
# Import Libraries
import numpy as np
from gekko import GEKKO
import pandas as pd
# Read Excel File To DataFrame
df = pd.read_excel('INPUT_ML_LIN000_00000.xlsx', header = None)
# Get A
AO = df.iloc[0][1]
# Get Number Of Drives
number_of_drives = df.iloc[1][1]
# First SPL Source
FSS = df.iloc[4][2]
# First Phase Source
FPS = df.iloc[4 + number_of_drives + 2][2]
# First G Source
FGS = df.iloc[4 + (number_of_drives * 2) + 3][3]
# Load G Values
g_matriz = []
for g_cursor in range(number_of_drives):
g_matriz.append(df.iloc[4 + (number_of_drives * 2) + 3 + g_cursor][3])
g_matriz = np.array(g_matriz)
print(AO, number_of_drives, FSS, FPS, FGS, g_matriz)
print(df)
def gekko_sistema():
find_solution = False
counter = 0.0
while not find_solution:
counter += 0.001
m = GEKKO() # create GEKKO model
G12 = m.Var() # define new variable default=0
G11 = m.Var() # define new variable default=0
G10 = m.Var() # define new variable default=0
G9 = m.Var() # define new variable default=0
G8 = m.Var() # define new variable default=0
G7 = m.Var() # define new variable default=0
G6 = m.Var() # define new variable default=0
G5 = m.Var() # define new variable default=0
G4 = m.Var() # define new variable default=0
G3 = m.Var() # define new variable default=0
G2 = m.Var() # define new variable default=0
G1 = m.Var() # define new variable default=0
DU = round(counter, 3) # Deviation
DL = round(counter, 3) # Deviation
A12 = m.CV(AO, AO - DL, AO + DU)
A11 = m.CV(AO, AO - DL, AO + DU)
A10 = m.CV(AO, AO - DL, AO + DU)
A9 = m.CV(AO, AO - DL, AO + DU)
A8 = m.CV(AO, AO - DL, AO + DU)
A7 = m.CV(AO, AO - DL, AO + DU)
A6 = m.CV(AO, AO - DL, AO + DU)
A5 = m.CV(AO, AO - DL, AO + DU)
A4 = m.CV(AO, AO - DL, AO + DU)
A3 = m.CV(AO, AO - DL, AO + DU)
A2 = m.CV(AO, AO - DL, AO + DU)
A1 = m.CV(AO, AO - DL, AO + DU)
#A = AO
m.Equations( \
[(-(10**(A12/20))**2) \
+ \
((10**((df.iloc[4][2 + 0] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[5][2 + 0] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[6][2 + 0] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[7][2 + 0] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[8][2 + 0] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[9][2 + 0] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[10][2 + 0] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[11][2 + 0] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[12][2 + 0] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[13][2 + 0] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[14][2 + 0] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[15][2 + 0] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 0])))**2 \
+ \
((10**((df.iloc[4][2 + 0] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[5][2 + 0] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[6][2 + 0] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[7][2 + 0] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[8][2 + 0] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[9][2 + 0] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[10][2 + 0] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[11][2 + 0] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[12][2 + 0] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[13][2 + 0] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[14][2 + 0] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 0])) + \
(10**((df.iloc[15][2 + 0] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 0])))**2 == 0, \
(-(10**(A11/20))**2) \
+ \
((10**((df.iloc[4][2 + 1] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[5][2 + 1] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[6][2 + 1] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[7][2 + 1] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[8][2 + 1] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[9][2 + 1] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[10][2 + 1] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[11][2 + 1] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[12][2 + 1] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[13][2 + 1] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[14][2 + 1] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[15][2 + 1] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 1])))**2 \
+ \
((10**((df.iloc[4][2 + 1] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[5][2 + 1] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[6][2 + 1] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[7][2 + 1] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[8][2 + 1] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[9][2 + 1] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[10][2 + 1] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[11][2 + 1] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[12][2 + 1] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[13][2 + 1] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[14][2 + 1] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 1])) + \
(10**((df.iloc[15][2 + 1] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 1])))**2 == 0, \
(-(10**(A10/20))**2) \
+ \
((10**((df.iloc[4][2 + 2] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[5][2 + 2] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[6][2 + 2] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[7][2 + 2] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[8][2 + 2] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[9][2 + 2] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[10][2 + 2] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[11][2 + 2] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[12][2 + 2] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[13][2 + 2] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[14][2 + 2] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[15][2 + 2] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 2])))**2 \
+ \
((10**((df.iloc[4][2 + 2] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[5][2 + 2] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[6][2 + 2] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[7][2 + 2] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[8][2 + 2] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[9][2 + 2] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[10][2 + 2] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[11][2 + 2] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[12][2 + 2] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[13][2 + 2] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[14][2 + 2] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 2])) + \
(10**((df.iloc[15][2 + 2] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 2])))**2 == 0, \
(-(10**(A9/20))**2) \
+ \
((10**((df.iloc[4][2 + 3] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[5][2 + 3] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[6][2 + 3] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[7][2 + 3] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[8][2 + 3] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[9][2 + 3] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[10][2 + 3] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[11][2 + 3] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[12][2 + 3] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[13][2 + 3] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[14][2 + 3] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[15][2 + 3] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 3])))**2 \
+ \
((10**((df.iloc[4][2 + 3] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[5][2 + 3] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[6][2 + 3] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[7][2 + 3] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[8][2 + 3] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[9][2 + 3] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[10][2 + 3] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[11][2 + 3] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[12][2 + 3] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[13][2 + 3] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[14][2 + 3] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 3])) + \
(10**((df.iloc[15][2 + 3] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 3])))**2 == 0, \
(-(10**(A8/20))**2) \
+ \
((10**((df.iloc[4][2 + 4] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[5][2 + 4] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[6][2 + 4] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[7][2 + 4] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[8][2 + 4] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[9][2 + 4] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[10][2 + 4] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[11][2 + 4] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[12][2 + 4] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[13][2 + 4] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[14][2 + 4] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[15][2 + 4] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 4])))**2 \
+ \
((10**((df.iloc[4][2 + 4] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[5][2 + 4] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[6][2 + 4] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[7][2 + 4] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[8][2 + 4] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[9][2 + 4] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[10][2 + 4] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[11][2 + 4] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[12][2 + 4] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[13][2 + 4] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[14][2 + 4] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 4])) + \
(10**((df.iloc[15][2 + 4] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 4])))**2 == 0, \
(-(10**(A7/20))**2) \
+ \
((10**((df.iloc[4][2 + 5] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[5][2 + 5] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[6][2 + 5] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[7][2 + 5] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[8][2 + 5] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[9][2 + 5] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[10][2 + 5] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[11][2 + 5] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[12][2 + 5] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[13][2 + 5] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[14][2 + 5] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[15][2 + 5] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 5])))**2 \
+ \
((10**((df.iloc[4][2 + 5] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[5][2 + 5] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[6][2 + 5] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[7][2 + 5] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[8][2 + 5] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[9][2 + 5] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[10][2 + 5] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[11][2 + 5] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[12][2 + 5] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[13][2 + 5] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[14][2 + 5] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 5])) + \
(10**((df.iloc[15][2 + 5] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 5])))**2 == 0, \
(-(10**(A6/20))**2) \
+ \
((10**((df.iloc[4][2 + 6] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[5][2 + 6] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[6][2 + 6] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[7][2 + 6] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[8][2 + 6] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[9][2 + 6] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[10][2 + 6] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[11][2 + 6] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[12][2 + 6] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[13][2 + 6] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[14][2 + 6] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[15][2 + 6] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 6])))**2 \
+ \
((10**((df.iloc[4][2 + 6] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[5][2 + 6] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[6][2 + 6] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[7][2 + 6] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[8][2 + 6] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[9][2 + 6] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[10][2 + 6] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[11][2 + 6] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[12][2 + 6] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[13][2 + 6] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[14][2 + 6] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 6])) + \
(10**((df.iloc[15][2 + 6] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 6])))**2 == 0, \
(-(10**(A5/20))**2) \
+ \
((10**((df.iloc[4][2 + 7] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[5][2 + 7] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[6][2 + 7] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[7][2 + 7] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[8][2 + 7] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[9][2 + 7] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[10][2 + 7] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[11][2 + 7] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[12][2 + 7] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[13][2 + 7] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[14][2 + 7] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[15][2 + 7] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 7])))**2 \
+ \
((10**((df.iloc[4][2 + 7] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[5][2 + 7] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[6][2 + 7] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[7][2 + 7] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[8][2 + 7] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[9][2 + 7] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[10][2 + 7] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[11][2 + 7] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[12][2 + 7] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[13][2 + 7] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[14][2 + 7] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 7])) + \
(10**((df.iloc[15][2 + 7] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 7])))**2 == 0, \
(-(10**(A4/20))**2) \
+ \
((10**((df.iloc[4][2 + 8] + G12) / 20) * np.cos(df.iloc[4 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[5][2 + 8] + G11) / 20) * np.cos(df.iloc[5 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[6][2 + 8] + G10) / 20) * np.cos(df.iloc[6 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[7][2 + 8] + G9) / 20) * np.cos(df.iloc[7 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[8][2 + 8] + G8) / 20) * np.cos(df.iloc[8 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[9][2 + 8] + G7) / 20) * np.cos(df.iloc[9 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[10][2 + 8] + G6) / 20) * np.cos(df.iloc[10 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[11][2 + 8] + G5) / 20) * np.cos(df.iloc[11 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[12][2 + 8] + G4) / 20) * np.cos(df.iloc[12 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[13][2 + 8] + G3) / 20) * np.cos(df.iloc[13 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[14][2 + 8] + G2) / 20) * np.cos(df.iloc[14 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[15][2 + 8] + G1) / 20) * np.cos(df.iloc[15 + number_of_drives + 2][2 + 8])))**2 \
+ \
((10**((df.iloc[4][2 + 8] + G12) / 20) * np.sin(df.iloc[4 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[5][2 + 8] + G11) / 20) * np.sin(df.iloc[5 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[6][2 + 8] + G10) / 20) * np.sin(df.iloc[6 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[7][2 + 8] + G9) / 20) * np.sin(df.iloc[7 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[8][2 + 8] + G8) / 20) * np.sin(df.iloc[8 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[9][2 + 8] + G7) / 20) * np.sin(df.iloc[9 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[10][2 + 8] + G6) / 20) * np.sin(df.iloc[10 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[11][2 + 8] + G5) / 20) * np.sin(df.iloc[11 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[12][2 + 8] + G4) / 20) * np.sin(df.iloc[12 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[13][2 + 8] + G3) / 20) * np.sin(df.iloc[13 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[14][2 + 8] + G2) / 20) * np.sin(df.iloc[14 + number_of_drives + 2][2 + 8])) + \
(10**((df.iloc[15][2 + 8] + G1) / 20) * np.sin(df.iloc[15 + number_of_drives + 2][2 + 8])))**2 == 0, \
(-(10**(A3/20))**2)
... cropped for Stack Overflow character limit
) # equations
m.options.MAX_ITER=250
m.options.IMODE = 3
#m.solve(disp=False) # solve
try:
m.solve(disp=False) # solve
find_solution = True
print(f'Solution Found: U {counter} L -{counter}')
except:
print(f'Solution Not Found: U {counter} L -{counter}')
return [G12.value[0], G11.value[0], G10.value[0], G9.value[0], G8.value[0], G7.value[0], G6.value[0], G5.value[0], G4.value[0], G3.value[0], G2.value[0], G1.value[0]] # print solution
result = gekko_sistema()
print(result)数据表
回答 1
Stack Overflow用户
发布于 2022-11-22 17:21:37
一个可能的问题是方程是否存在多个局部解。另一个需要检查的潜在问题是，他们是否都报告了一个成功的解决方案。尝试给出相同的初始条件给两者。请张贴产生不同结果的代码。因为问题中没有代码，下面是一个例子。
Python优化
import numpy as np
from scipy.optimize import fsolve
def myFunction(z):
x = z[0]
y = z[1]
w = z[2]
F = np.empty((3))
F[0] = x**2+y**2-20
F[1] = y - x**2
F[2] = w + 5 - x*y
return F
zGuess = np.array([1,1,1])
z = fsolve(myFunction,zGuess)
print(z)Python
from gekko import GEKKO
m = GEKKO()
x,y,w = [m.Var(1) for i in range(3)]
m.Equations([x**2+y**2==20,\
y-x**2==0,\
w+5-x*y==0])
m.solve(disp=False)
print(x.value,y.value,w.value)这两者产生了相同的解决方案。这两个包使用不同的解决方案技术。
对编辑的响应
源代码不存在重大问题，但10**(a*variable+b)是一个非常非线性的方程。另一种优化解决方案是编译的可执行文件，因此不能直接进行比较。以下是一些建议：
- 取方程两边的
m.log10()可能有助于更快地找到一个解。
，
- ，求解器，每个循环最多运行250个迭代和反增量，以扩展变量的上限和下限。当找到一个解决方案并且问题是可行时，迭代就停止了。
counter += 0.001
m = GEKKO() # create GEKKO model
G12 = m.Var() # define new variable default=0
G11 = m.Var() # define new variable default=0
G10 = m.Var() # define new variable default=0
G9 = m.Var() # define new variable default=0
G8 = m.Var() # define new variable default=0
G7 = m.Var() # define new variable default=0
G6 = m.Var() # define new variable default=0
G5 = m.Var() # define new variable default=0
G4 = m.Var() # define new variable default=0
G3 = m.Var() # define new variable default=0
G2 = m.Var() # define new variable default=0
G1 = m.Var() # define new variable default=0
DU = round(counter, 3) # Deviation
DL = round(counter, 3) # Deviation
A12 = m.CV(AO, AO - DL, AO + DU)
A11 = m.CV(AO, AO - DL, AO + DU)
A10 = m.CV(AO, AO - DL, AO + DU)
A9 = m.CV(AO, AO - DL, AO + DU)
A8 = m.CV(AO, AO - DL, AO + DU)
A7 = m.CV(AO, AO - DL, AO + DU)
A6 = m.CV(AO, AO - DL, AO + DU)
A5 = m.CV(AO, AO - DL, AO + DU)
A4 = m.CV(AO, AO - DL, AO + DU)
A3 = m.CV(AO, AO - DL, AO + DU)
A2 = m.CV(AO, AO - DL, AO + DU)
A1 = m.CV(AO, AO - DL, AO + DU)关于本节的几点建议是：
A12
- expand用
m.Var()代替m.CV()对A1的上限和下限求出可行解更快的
- ，目前有24个变量具有
A1toA12和G1，您需要求解12个方程和12个变量，然后使用IMODE=1检查freedom
- there的度数没有客观函数来指导选择附加的12个变量<代码>H 238/代码>
F 239
优化代码缺少Coverage.xlsx。我建议您为优化问题创建一个新的问题。本课题的重点是对这12个方程的模拟和求解。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/74536189
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