Vector3d p_desired = INIT_T.block<3, 1>(0, 3) + Vector3d{{-0.01, 0, 0}};

这是一行 C++ 代码，其中包括一个名为 p_desired 的 Vector3d 变量，其值为 INIT_T 矩阵的第 0 列到第 2 列（即前三列）和第 3 行（即第四行）组成的 3x1 向量加上一个值为 {-0.01, 0, 0} 的向量。这段代码可能用于机器人运动规划中，用于计算机器人末端执行器的期望位置。

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D_loss_temp = -tf.reduce_mean(M * tf.math.log(D_prob + 1e-8) \ + (1 - M) * tf.math.log(1. - D_prob + 1e-8))

This is a line of code that calculates the loss for a discriminator model in a conditional generative adversarial network (cGAN). The cGAN consists of two models, a generator and a discriminator, that are trained together to generate output images that match a desired input condition.

The D_loss_temp variable represents the temporary value of the discriminator loss function. The loss function is calculated using the binary cross-entropy formula, which compares the predicted probability of a real or fake image with the true label.

The tf.reduce_mean function calculates the mean value of the loss over all the samples in a batch.

The M variable represents the mask that is applied to the loss function for the conditional aspect of the cGAN. It is a binary matrix that is the same size as the output image, where a value of 1 represents the areas of the image that need to be generated and a value of 0 represents the areas that can be left unchanged.

The D_prob variable represents the predicted probability of the discriminator model that the generated image is real. The 1e-8 term is added to prevent numerical instability when taking the logarithm of small values.

Overall, this line of code is a crucial step in updating the weights of the discriminator model during the training process of a cGAN.

% 设定模糊控制器参数T_min = 18;T_max = 28;T_desired = 25;P_min = -1;P_max = 1;% 设定模糊控制器输入和输出变量T = linspace(T_min, T_max, 100);P = linspace(P_min, P_max, 100);T_error = linspace(-5, 5, 100);P_change = linspace(-0.5, 0.5, 100);% 设定模糊控制器规则库rule_list = [1, 1, 1, 1; 1, 2, 1, 1; 2, 2, 2, 1; 2, 3, 3, 1; 3, 3, 3, 1; 3, 4, 4, 1; 4, 4, 4, 1; 4, 4, 4, 1];% 设定初始值T_current = 20;P_current = 0;% 设定时间步长和仿真时间dt = 0.1;t_end = 100;% 计算模糊控制量for t = 0:dt:t_end % 计算误差 T_error_current = T_desired - T_current; % 计算模糊控制量 for i = 1:length(T) if T_error_current >= T(i) && T_error_current < T(i+1) A1 = (T(i+1) - T_error_current) / (T(i+1) - T(i)); A2 = (T_error_current - T(i)) / (T(i+1) - T(i)); for j = 1:length(P) if P_current >= P(j) && P_current < P(j+1) B1 = (P(j+1) - P_current) / (P(j+1) - P(j)); B2 = (P_current - P(j)) / (P(j+1) - P(j)); rule = rule_list(i,j); P_change_current = P_change(rule); break; end end break; end end % 更新空调功率 P_current = P_current + P_change_current; % 更新当前温度 T_current = T_current + P_current * dt;end% 绘制温度变化曲线plot(0:dt:t_end, T_current);xlabel('Time (s)');ylabel('Temperature (C)');title('Temperature Control with Fuzzy Controller');

这段代码是一个基于模糊控制的温度控制系统的仿真代码。其中，模糊控制器的输入变量包括当前温度偏差(T_error)和当前空调功率(P_current)，输出变量为空调功率变化量(P_change)。代码中的模糊控制器规则库(rule_list)是一个8x4的矩阵，其中每一行代表一个模糊规则，第一列和第二列分别表示T_error和P_current所属的模糊集合，第三列表示P_change所属的模糊集合，第四列表示该规则的权重。在每个时间步长中，根据当前温度偏差和空调功率的模糊集合，从规则库中选择相应的规则，并计算出相应的空调功率变化量，从而更新当前空调功率和当前温度，并绘制温度随时间的变化曲线。