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相关概念视频

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

226
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
226
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Sampling Theorem01:15

Sampling Theorem

324
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
324
Confidence Intervals01:21

Confidence Intervals

6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.2K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

191
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
191
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

204
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
204

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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基于样本的连续近似方法来构建间隔神经网络.

Xun Shen, Tinghui Ouyang, Kazumune Hashimoto

    IEEE transactions on neural networks and learning systems
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    概括
    此摘要是机器生成的。

    本研究引入了一种用于训练间隔神经网络 (INN) 的新方法,用于量化安全关键应用中的不确定性. 该方法确保了可靠的预测,保证了信心水平,提高了对错误的稳定性.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 量化神经网络的不确定性对于安全关键的应用,如对抗噪声预测至关重要.
    • 间隔神经网络 (INN) 为不确定性量化提供预测间隔.

    研究的目的:

    • 将INN的培训制定为一个机会受限的优化问题.
    • 为INN培训中难以解决的机会受限制的优化问题开发近似方法.
    • 确保开发的近似方法产生最佳的INN,保证可信度水平.

    主要方法:

    • 制定INN培训作为一个机会受限的优化问题.
    • 采用基于样本的连续近似方法来解决难以解决的优化问题.
    • 证明近似方法的统一收.
    • 用有限样本调查近似值的可靠性.

    主要成果:

    • 机会受约束优化的最佳解决方案自然形成一个INN,在所需的信心水平上提供最紧密的预测间隔.
    • 基于样本的近似方法实现了统一的趋同,始终产生最佳的INN.
    • 该研究提供了有限样本的违规概率,确保近似可靠性.

    结论:

    • 拟议的方法有效地训练INN在安全关键应用中的不确定性量化.
    • 与现有方法相比,INN方法显著提高了回归和无监督异常检测的性能.
    • 该研究通过数值示例和风力发电异常检测案例研究来验证有效性.