LQR-UFO实验： matlab 官方代码 原视频网址 test1 我们调节 Q矩阵，penalize angular error (角度误差代价) 为 1，penalize angular rate...结果如下所示，在R penalize angular error 代价较大得情况下，使用lqr，Angular Error最后能收敛，并且没有出现较大幅度的超调。...test2 调节Q矩阵，penalize angular error (角度误差代价) 为 1，penalize angular rate 为100 (角速度代价) 结果如下所示，可以看到，加速度比较快的达到收敛...test3 调节R矩阵 Penalize thruster effort 执行器代价为3 结果如下所示，燃烧的燃料明显少了 【Advanced控制理论】8_LQR 控制器_状态空间系统Matlab
Joiners.equal(Queen::getColumn), Joiners.lessThan(Queen::getId)) .penalize...Joiners.equal(Queen::getRow), Joiners.lessThan(Queen::getId)) .penalize...getAscendingDiagonalIndex), Joiners.lessThan(Queen::getId)) .penalize...getDescendingDiagonalIndex), Joiners.lessThan(Queen::getId)) .penalize
return factory.from(Shift.class) .filter(Shift::isEmployeeAnn) .penalize....filter((computer, requiredCpuPower) -> requiredCpuPower > computer.getCpuPower()) .penalize...因此，可以看到，factory除了过from操作获得所有Process对象，通过filter对Process进行过滤，通过penalize进行计分外。...因此，在filter方法中，就找出那些超出CPU能力的Computer（即分组），在penalize方法中，对整所有超出CPU需求中的计算进行扣分，扣分值是超出部分。....filter((computer, requiredCpuPower) -> requiredCpuPower > computer.getCpuPower()) .penalize
Joiners.lessThan(Lesson::getId)) // ...对于每一对满足以上关联条件的课程，都使用一个硬约束权重来进行处罚（负分） .penalize...Joiners.equal(Lesson::getTeacher), Joiners.lessThan(Lesson::getId)) .penalize...Lesson::getStudentGroup), Joiners.lessThan(Lesson::getId)) .penalize
用训练集来建树， 用test来估计叶节点variance，penalize小的叶结点带来的高方差，然后用叶节点上train和val的差异来penalize损失函数,以下 (lambda) 控制penalty
Since % information criteria penalize models with additional parameters, AIC and % BIC are model order
// Update upstream sRTT on UDP queries, penalize it if it fails if !
(Default: 64, 0 = disabled, -1 = num_ctx) repeat_penalty: 1.2 ## Sets how strongly to penalize repetitions...A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will
1e-3 # likely crashed/ close to off track if not all_wheels_on_track: # Penalize...the car goes off track reward = 1e-3 elif speed < SPEED_THRESHOLD: # Penalize
Compare with Squared Error (F), exponential loss is monotonically decreasing with \(yf(x)\), it doesn't penalize...Both exponential loss and log-likelihood penalize large negative margin \(yf(x)\) - very wrong prediction...Exponential loss penalize negative margin even more, makes it sensitive to noise, such as mislabeled
我们能使用以下代码来得到均方误差的值： metrics.mean_squared_error(y_true, y_pred) 89.20901624683765 You'll notice that this code will penalize
The set-level evaluation, unlike the sequence-level, does not penalize for time misalignment of a rhythm
sg = com$membership + 1 V(g1)$color = cl[V(g1)$sg] 偏相关分析 res=glasso.miss(pm_sc,rho=0.5,emIter=10 ,penalize.diagonal
It's similar to Ridge Regression in the sense that we penalize our regression by some amount, and it's
, 在AG News数据集上选择更高层的效果更好，不过感觉这个参数应该是task specific的最小熵正则 MixText进一步加入了最小熵原则，在无标注数据上，通过penalize大于\(\gamma
但是现在还存在一个问题，会over-penalize realistic details in patch C，轻度惩罚A和B，尤其在早期时。为了缓解这个问题，作者选用了即时梯度下降。
hanning window)，也叫余弦窗【可以看这里】，论文中说是增加惩罚：Online, ... and a cosine window is added to the score map to penalize..., 1, 17, 17] responses = responses.squeeze(1).cpu().numpy() # [3, 17, 17] # upsample responses and penalize
Three different GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to penalize
领域外到内的迁移，主要需要解决样本差异性问题，毕竟最终目标是希望帮助领域内文本学到合理的文本表达，所以需要penalize和目标领域差异过大的领域外样本。...v_x 和 v_{IN} 的欧式距离 Polynomial Kernel： v_x 和 v_{IN} 的cosine距离 领域内未标注样本的半监督学习，因为是直接用模型预测来做真实label，因此需要penalize
作为一个练习，尝试使用相同的手段——对错误的信号进行惩罚（原文是penalyzing，但没有这个单词的感觉，我觉得是之前的penalize的ing形式）损失函数——但运用均方误差（MSE）,因为对于回归问题来说这个损失函数是更健全的
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