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Videos de Conceptos Relacionados

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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Implementación de modelos de espacio de estados para el procesamiento de secuencias de eventos en computación en

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
Resumen
Este resumen es generado por máquina.

Los modelos de espacio de estados (SSM) son ahora eficientes en hardware nuevo. Esta investigación integra SSM con hardware de computación en memoria para el procesamiento en tiempo real y basado en eventos en tareas de IA.

Palabras clave:
modelos de espacio de estadoscomputación en memoriaprocesamiento de eventosinteligencia artificialhardware de IAprocesamiento en tiempo realvisión basada en eventosaudio basado en eventosredes neuronalessistemas biológicosco-diseño de algoritmos y hardwaredatos sensoriales

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Área de la Ciencia:

  • Inteligencia Artificial
  • Ingeniería Informática
  • Neurociencia

Sus antecedentes:

  • Los modelos de espacio de estados (SSM) ofrecen capacidades avanzadas de procesamiento de secuencias largas.
  • Los SSM generalizan las redes recurrentes y convolucionales, imitando las funciones de los sistemas biológicos.
  • Las implementaciones existentes de SSM enfrentan desafíos en eficiencia energética y procesamiento en tiempo real.

Objetivo del estudio:

  • Implementar modelos de espacio de estados (SSM) en hardware de computación en memoria energéticamente eficiente.
  • Lograr el procesamiento en tiempo real y basado en eventos para aplicaciones de IA.
  • Explorar el co-diseño de algoritmos y hardware para un rendimiento mejorado.

Principales métodos:

  • SSM repareterizados para coeficientes de valor real y constantes de decaimiento compartidas.
  • Aprovechamiento de la dinámica del dispositivo y parámetros de transición de estado diagonalizados.
  • Implementación de la evolución del estado de forma nativa en sistemas de computación en memoria basados en redes de conmutación con memristores.

Principales resultados:

  • Se logró una alta precisión en tareas de IA utilizando el sistema propuesto.
  • Se demostró una eficiencia energética significativa en comparación con los métodos tradicionales.
  • Se habilitó el procesamiento totalmente asíncrono para visión y audio basados en eventos.

Conclusiones:

  • El co-diseño de algoritmos y hardware permite una implementación eficiente de SSM.
  • El sistema proporciona una vía para el procesamiento de IA en tiempo real y de baja potencia.
  • Este enfoque es adecuado para tareas de datos sensoriales basados en eventos.