帮忙优化一下这段代码i=0:0.1:45 ; i0=i*pi/180; r1=70*pi/180; m1=195; K=1500; L=3025; qwz=acot(cot(i0)-K/L); %期望值 P1=sin(r1+i0); Q1=(K/m1)*(2*cos(r1)-cos(r1+i0))-cos(2*r1); R1=sqrt((K/m1)^2+1-2*(K/m1)*cos(r1+i0)); yhq=real(r1-asin(P1./R1)-acos(Q1./R1)); %实际值 plot(i,qwz,'b-'); grid on; hold on; %画在同一张图上 plot(i,yhq,'r+'); hold on; xlabel('外轮转角'); ylabel('内轮转角'); legend('理论转角','实际转角'); title('理论转角VS实际转角');

这段代码中有一些重复计算的部分，可以将它们提前计算出来，以提高代码的效率。此外，可以考虑使用矩阵运算来避免使用循环。

以下是优化后的代码：

i = 0:0.1:45;
i0 = i * pi / 180;
r1 = 70 * pi / 180;
m1 = 195;
K = 1500;
L = 3025;

cot_i0 = cot(i0); % 提前计算 cot(i0)
P1 = sin(r1 + i0);
cos_r1 = cos(r1);
cos_r1_i0 = cos(r1 + i0);
Q1 = (K/m1)*(2*cos_r1-cos_r1_i0)-cos(2*r1);
K_div_m1 = K/m1;
R1 = sqrt(K_div_m1^2+1-2*K_div_m1*cos_r1_i0);
ywq = r1 - asin(P1./R1) - acos(Q1./R1);

plot(i, acot(cot_i0-K/L), 'b-', i, real(ywq), 'r+');
grid on;
xlabel('外轮转角');
ylabel('内轮转角');
legend('理论转角', '实际转角');
title('理论转角 VS 实际转角');

这样，代码会更加简洁和高效。

相关问题

你是一名专业的算法大师，请帮我使用matlab编写代码求出电机1和电机2的最大转矩电流比(MTPA)曲线和最大转矩电压比(MTPV)曲线，计算不考虑饱和特性 已知：电机1参数分别如下:44kW PMSM:定子相电阻R=4m2定子链相绕组永磁磁链峰值;=0055Wb电机极对数p=4，转动惯量J=0.048kg·m，定子绕组相漏电感L=2pHd轴励磁电感L;=88pH，q轴励磁电感L=300pH，额定转速为 4000r/min，峰值转矩temax=210N·m。不考虑饱和特性。电机2参数分别如下：2100kW PMSM:定子相电阻R=83m定子链相绕组永磁磁链峰值=0.071Wb，电机极对数p=4，转动惯量J=0.1kgm，定子绕组相漏电感L=30uHd轴励磁电感L;=174uHq轴励磁电感 L=293pH，额定转速为4700r/min峰值转矩 temax256N·m

好的，根据您提供的电机参数，我可以帮您编写求解 MTPA 曲线和 MTPV 曲线的 Matlab 代码。代码如下：

% 电机1参数
P1 = 44e3;      % 额定功率，单位：W
R1 = 4e-3;      % 定子相电阻，单位：Ω
Ld1 = 88e-6;    % d 轴励磁电感，单位：H
Lq1 = 300e-6;   % q 轴励磁电感，单位：H
J1 = 0.048;     % 转动惯量，单位：kg·m^2
p1 = 4;         % 极对数
Flux1 = 0.055;  % 定子链相绕组永磁磁链峰值，单位：Wb
N1 = 4000;      % 额定转速，单位：rpm
Tmax1 = 210;    % 峰值转矩，单位：N·m

% 电机2参数
P2 = 2100e3;    % 额定功率，单位：W
R2 = 83e-3;     % 定子相电阻，单位：Ω
Ld2 = 174e-6;   % d 轴励磁电感，单位：H
Lq2 = 293e-6;   % q 轴励磁电感，单位：H
J2 = 0.1;       % 转动惯量，单位：kg·m^2
p2 = 4;         % 极对数
Flux2 = 0.071;  % 定子链相绕组永磁磁链峰值，单位：Wb
N2 = 4700;      % 额定转速，单位：rpm
Tmax2 = 256;    % 峰值转矩，单位：N·m

% 计算电机1的 MTPA 曲线
i1 = 0:0.01:1;                      % 电机1的电流比
w1 = N1 * pi / 30;                  % 电机1的角速度，单位：rad/s
T1 = 1.5 * p1 * Flux1 * i1 / w1;    % 电机1的转矩，单位：N·m
Mtpa1 = Tmax1 * ones(size(i1));     % 电机1的最大转矩电流比
for k = 1:length(i1)
    if T1(k) > Tmax1
        Mtpa1(k) = i1(k) * Tmax1 / T1(k);
    end
end

% 计算电机2的 MTPA 曲线
i2 = 0:0.01:1;                      % 电机2的电流比
w2 = N2 * pi / 30;                  % 电机2的角速度，单位：rad/s
T2 = 1.5 * p2 * Flux2 * i2 / w2;    % 电机2的转矩，单位：N·m
Mtpa2 = Tmax2 * ones(size(i2));     % 电机2的最大转矩电流比
for k = 1:length(i2)
    if T2(k) > Tmax2
        Mtpa2(k) = i2(k) * Tmax2 / T2(k);
    end
end

% 计算电机1的 MTPV 曲线
v1 = 0:0.01:1;                      % 电机1的电压比
Mtpv1 = Tmax1 * ones(size(v1));     % 电机1的最大转矩电压比
for k = 1:length(v1)
    if v1(k) > sqrt(R1 / (Ld1 * w1))
        Mtpv1(k) = sqrt((Tmax1 * sqrt(Ld1 / R1)) / (1.5 * p1 * Flux1 * w1)) / v1(k);
    end
end

% 计算电机2的 MTPV 曲线
v2 = 0:0.01:1;                      % 电机2的电压比
Mtpv2 = Tmax2 * ones(size(v2));     % 电机2的最大转矩电压比
for k = 1:length(v2)
    if v2(k) > sqrt(R2 / (Ld2 * w2))
        Mtpv2(k) = sqrt((Tmax2 * sqrt(Ld2 / R2)) / (1.5 * p2 * Flux2 * w2)) / v2(k);
    end
end

% 绘制 MTPA 曲线和 MTPV 曲线
figure;
subplot(2, 1, 1);
plot(i1, Mtpa1, 'r', i2, Mtpa2, 'b');
title('MTPA曲线');
xlabel('电流比');
ylabel('最大转矩电流比');
legend('电机1', '电机2');
subplot(2, 1, 2);
plot(v1, Mtpv1, 'r', v2, Mtpv2, 'b');
title('MTPV曲线');
xlabel('电压比');
ylabel('最大转矩电压比');
legend('电机1', '电机2');

这段代码会绘制出两个子图，分别是 MTPA 曲线和 MTPV 曲线，其中红色的是电机1的曲线，蓝色的是电机2的曲线。您可以根据需要自行修改代码中的参数和绘图方式。

帮我写WOA优化SVR的python代码

当然可以帮你编写WOA优化SVR的Python代码。下面是一个简单示例：

import numpy as np
from sklearn.svm import SVR
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from math import sin, pi

# Whale Optimization Algorithm (WOA) implementation
def woa_optimization(X, y, population_size, max_iterations):
    num_features = X.shape[1]
    num_samples = X.shape[0]
    
    # Initialize whale population
    population = np.random.uniform(-1, 1, (population_size, num_features))
    
    # Initialize convergence curve
    convergence_curve = np.zeros(max_iterations)
    
    # Initialize best solution
    best_solution = None
    best_fitness = float('inf')
    
    for iteration in range(max_iterations):
        a = 2 - iteration * ((2) / max_iterations)  # a decreases linearly from 2 to 0
        
        for i in range(population_size):
            r1 = np.random.random()  # r1 is a random number in [0,1]
            r2 = np.random.random()  # r2 is a random number in [0,1]
            
            A = 2 * a * r1 - a  # Equation (2.3)
            C = 2 * r2  # Equation (2.4)
            
            # Update position of the current whale
            D = np.abs(C * population[i] - population)
            b = 1
            
            x_rand = population[np.random.randint(population_size)]
            
            # Equation (2.5)
            D_2 = np.abs(b * x_rand - population)
            
            new_position = np.zeros(num_features)
            for j in range(num_features):
                # Equation (2.7)
                if D_2[j] >= D[j]:
                    new_position[j] = x_rand[j] - A * D_2[j]
                else:
                    new_position[j] = population[i][j] - A * D[j]
            
            # Boundary handling
            new_position = np.clip(new_position, -1, 1)
            
            # Update fitness of the current whale
            new_fitness = svr_fitness(new_position, X, y)
            
            # Update best solution
            if new_fitness < best_fitness:
                best_solution = new_position
                best_fitness = new_fitness
            
            # Update convergence curve
            convergence_curve[iteration] = best_fitness
        
        population = new_position
    
    return best_solution, best_fitness, convergence_curve

# Fitness function
def svr_fitness(position, X, y):
    svr = SVR()
    svr.fit(X, y)
    y_pred = svr.predict(X)
    mse = np.mean((y - y_pred) ** 2)
    return mse

# Generate a regression dataset
X, y = make_regression(n_samples=100, n_features=1, noise=0.1)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Perform WOA optimization on SVR
best_solution, best_fitness, convergence_curve = woa_optimization(X_train, y_train, population_size=10, max_iterations=100)

# Fit SVR with the best solution found
svr_best = SVR()
svr_best.fit(X_train, y_train)
y_pred_train = svr_best.predict(X_train)
y_pred_test = svr_best.predict(X_test)

# Print results
print("Best Solution:", best_solution)
print("Best Fitness:", best_fitness)

# Plot convergence curve
import matplotlib.pyplot as plt
plt.plot(convergence_curve)
plt.xlabel("Iteration")
plt.ylabel("Fitness")
plt.title("WOA Optimization Convergence")
plt.show()

在上述代码中，我们先定义了WOA优化算法的实现，然后使用SVR作为目标函数进行优化。代码中还包含了生成回归数据集、拆分数据集、以及绘制收敛曲线的部分。

这段代码可以帮助你完成WOA优化SVR的Python实现，你可以根据自己的需求进行修改和扩展。希望能对你有所帮助！