clear all; clc; % 载入数据 data = xlsread('Copy_of_数据集.xlsx'); input = data((1:120), 2:6)'; output = data((1:120), 7:9)'; % 划分训练集和测试集 input_train = input(:, 1:80); output_train = output(:, 1:80); input_test = input(:, 81:100); output_test = output(:, 81:100); % 归一化 [input_train_n, input_ps] = mapminmax(input_train, -1, 1); [output_train_n, output_ps] = mapminmax(output_train, -1, 1); % 建立模型 input_size = size(input_train_n, 1); hidden_size = 10; output_size = size(output_train_n, 1); net = newff(input_train_n, output_train_n, hidden_size, {'tansig','purelin'}, 'trainlm'); net.trainParam.epochs = 15000; net.trainParam.lr = 0.01; net.trainParam.goal = 0.0001; % 训练模型 [net, tr] = train(net, input_train_n, output_train_n); % 测试模型 input_test_n = mapminmax('apply', input_test, input_ps); output_test_n = mapminmax('apply', output_test, output_ps); output_pred_n = sim(net, input_test_n); %% 反归一化 output_test_pred = mapminmax('reverse', output_pred_n, output_ps); output_test_pred = round(output_test_pred); % 四舍五入取整 % 使用测试集评估网络性能 pos_pred = sim(net, input_test_n); % 预测位置 ori_pred = sim(net, input_test_n); % 预测姿态 pos_error = pos_pred - output_test(1,:); % 位置误差 ori_error = ori_pred - output_test(1,:); % 姿态误差 mse_pos = mean(pos_error.^2); % 位置均方误差 mse_ori = mean(ori_error.^2); % 姿态均方误差 % 使用附加测试集评估网络性能 additional_test_data = [theta([6, 12, 18], :), actual_poses([6, 12, 18], :)]; pos_pred = sim(net, mapminmax('apply', additional_test_data(:, 1:input_size), input_ps)); % 预测位置 ori_pred = sim(net, mapminmax('apply', additional_test_data(:, 1:input_size), input_ps)); % 预测姿态 pos_error = pos_pred - additional_test_data(:, input_size+1:input_size+output_size); % 位置误差 ori_error = ori_pred - additional_test_data(:, input_size+output_size+1:end); % 姿态误差 mse_pos_additional = mean(pos_error.^2); % 位置均方误差 mse_ori_additional = mean(ori_error.^2); % 姿态均方误差 % 调整维度为 2 x 10 % 绘制预测结果和真实结果的对比图 figure; plot(output_test(1,:), 'bo-'); hold on; plot(output_test_pred(1,:)', 'r*-'); % 注意转置 legend('真实结果', '预测结果'); xlabel('样本编号'); ylabel('输出值'); title('预测结果和真实结果');则合格代码报错帮我修改正确

在你提供的代码中，有一些变量没有定义，例如`theta`和`actual_poses`，我将其注释掉。此外，您在使用`mapminmax`函数时，应该将附加测试数据也归一化。下面是修改后的代码：

clear all; 
clc; 

% 载入数据 
data = xlsread('Copy_of_数据集.xlsx'); 
input = data((1:120), 2:6)'; 
output = data((1:120), 7:9)'; 

% 划分训练集和测试集 
input_train = input(:, 1:80); 
output_train = output(:, 1:80); 
input_test = input(:, 81:100); 
output_test = output(:, 81:100); 

% 归一化 
[input_train_n, input_ps] = mapminmax(input_train, -1, 1); 
[output_train_n, output_ps] = mapminmax(output_train, -1, 1); 

% 建立模型 
input_size = size(input_train_n, 1); 
hidden_size = 10; 
output_size = size(output_train_n, 1); 
net = newff(input_train_n, output_train_n, hidden_size, {'tansig','purelin'}, 'trainlm'); 
net.trainParam.epochs = 15000; 
net.trainParam.lr = 0.01; 
net.trainParam.goal = 0.0001; 

% 训练模型 
[net, tr] = train(net, input_train_n, output_train_n); 

% 测试模型 
input_test_n = mapminmax('apply', input_test, input_ps); 
output_test_n = mapminmax('apply', output_test, output_ps); 
output_pred_n = sim(net, input_test_n); 

%% 反归一化 
output_test_pred = mapminmax('reverse', output_pred_n, output_ps); 
output_test_pred = round(output_test_pred); % 四舍五入取整 

% 使用测试集评估网络性能 
pos_pred = sim(net, input_test_n); % 预测位置 
ori_pred = sim(net, input_test_n); % 预测姿态 
pos_error = pos_pred - output_test(1,:); % 位置误差 
ori_error = ori_pred - output_test(1,:); % 姿态误差 
mse_pos = mean(pos_error.^2); % 位置均方误差 
mse_ori = mean(ori_error.^2); % 姿态均方误差 

% 使用附加测试集评估网络性能 
% additional_test_data = [theta([6, 12, 18], :), actual_poses([6, 12, 18], :)]; 
additional_test_data = input(101:120,:)';
additional_test_data_n = mapminmax('apply', additional_test_data, input_ps); 
pos_pred = sim(net, additional_test_data_n); % 预测位置 
ori_pred = sim(net, additional_test_data_n); % 预测姿态 
pos_error = pos_pred - output(1,:); % 位置误差 
ori_error = ori_pred - output(1,:); % 姿态误差 
mse_pos_additional = mean(pos_error.^2); % 位置均方误差 
mse_ori_additional = mean(ori_error.^2); % 姿态均方误差 

% 调整维度为 2 x 10 

% 绘制预测结果和真实结果的对比图 
figure; 
plot(output_test(1,:), 'bo-'); 
hold on; 
plot(output_test_pred(1,:)', 'r*-'); % 注意转置 
legend('真实结果', '预测结果'); 
xlabel('样本编号'); 
ylabel('输出值'); 
title('预测结果和真实结果');