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相关概念视频

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

395
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
395
Linear time-invariant Systems01:23

Linear time-invariant Systems

258
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
258
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
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State Space Representation01:27

State Space Representation

208
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
208
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

211
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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相关实验视频

Updated: Jul 3, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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使用离散时间索博列夫网络的混乱动态系统的数据驱动学习.

Connor Kennedy1, Trace Crowdis1, Haoran Hu1

  • 1Department of Mathematics & Statistics, University of Massachusetts, Amherst, MA 01003, USA.

Neural networks : the official journal of the International Neural Network Society
|February 15, 2024
PubMed
概括

我们开发了一个新的神经网络损失函数,即离散时间索博列夫网络 (DTSN),以改善动态系统的预测. 通过最大限度地减少噪音,DTSN提高了准确性,特别是在混乱系统中.

关键词:
一个混乱的系统.这是LSTM的LSTM.洛伦茨系统 洛伦茨系统神经网络的神经网络的神经网络物理信息神经网络的神经网络预测 预测 预测

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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科学领域:

  • 计算科学 计算科学
  • 应用数学 应用数学 应用数学
  • 机器学习 机器学习

背景情况:

  • 预测动态系统具有挑战性,特别是对于对初始条件敏感的混乱系统.
  • 现有的神经网络方法经常与噪音作斗争,需要衍生信息.

研究的目的:

  • 介绍离散时间索博列夫网络 (DTSN) 作为动态系统预测的新损失函数.
  • 与标准的平均平方误差 (MSE) 和物理信息神经网络 (PINN) 损失相比,评估DTSN的有效性.

主要方法:

  • 开发了DTSN,一个数据驱动和架构不可知损失函数,使用时间索波列夫规范最小化变化差异.
  • 应用DTSN与长短期存储器 (LSTM) 和变压器架构,以离散近似的洛伦兹-63和丘亚电路系统.

主要成果:

  • 对于LSTM和变压器架构,DTSN显著提高了预测准确度.
  • 与MSE损失相比,DTSN表现优越,需要的信息比PINN损失少.
  • 使用DTSN.计算时间没有明显增加.

结论:

  • DTSN提供了一种强大的数据驱动方法,用于增强基于神经网络的动态系统预测.
  • 这种方法对混乱系统尤其有利,因为它具有最小化噪声的特性.
  • DTSN为现有方法提供了一个可行的替代方案,可以提高准确性,而无需显著的计算开销.