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相关概念视频

State Space Representation01:27

State Space Representation

519
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...
519
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

282
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
282
State Space to Transfer Function01:21

State Space to Transfer Function

552
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
552
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

426
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
426
Transfer Function to State Space01:23

Transfer Function to State Space

748
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
748
Linear time-invariant Systems01:23

Linear time-invariant Systems

863
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...
863

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Updated: Jan 13, 2026

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
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状态空间模型的内存计算实现,用于事件序列处理.

Xiaoyu Zhang1, Mingtao Hu1, Sen Lu1

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.

Nature communications
|January 9, 2026
PubMed
概括
此摘要是机器生成的。

国家空间模型 (SSM) 现在在新硬件上是高效的. 这项研究将SSM与内存计算硬件集成,用于人工智能任务中的实时事件驱动处理.

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科学领域:

  • 人工智能的人工智能
  • 计算机工程 计算机工程
  • 神经科学是一个神经科学.

背景情况:

  • 国家空间模型 (SSM) 提供先进的长序处理能力.
  • SSM 将循环和卷积网络泛化,模仿生物系统的功能.
  • 现有的SSM实施面临能源效率和实时处理方面的挑战.

研究的目的:

  • 在节能计算内存硬件上实现状态空间模型 (SSM).
  • 为实现人工智能应用程序的实时,事件驱动的处理.
  • 探索算法和硬件共同设计以提高性能.

主要方法:

  • 对实值系数和共享衰变常数进行重新参数化的SSM.
  • 杆设备动态和对角化状态过渡参数.
  • 在基于交叉的内存计算系统中与memristors本地实现了状态演变.

主要成果:

  • 使用拟议的系统,在AI任务中实现了高精度.
  • 与传统方法相比,证明了显著的能源效率.
  • 启用完全异步处理以实现基于事件的视觉和音频.

结论:

  • 算法和硬件的共同设计使SSM的高效实施成为可能.
  • 该系统为实时,低功耗的人工智能处理提供了一条途径.
  • 这种方法适用于基于事件的感觉数据任务.