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State Space Representation

State Space Representation

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...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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 of...

Neural Control of Respiration

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...

State Space to Transfer Function

State Space to Transfer Function

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:

Transfer Function to State Space

Transfer Function to State Space

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...

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Dinámica de secuencias multimodales y optimización de la convergencia en redes LSTM de doble flujo para la estimación

¹Department of Public Physical Education, China Academy of Art, Hangzhou, Zhejiang, China.

Frontiers in neurorobotics

|February 23, 2026

Resumen

Este estudio presenta una red de memoria a corto plazo de doble flujo (DS-LSTM) basada en atención para el entrenamiento personalizado de voleibol. El modelo mejora el modelado de secuencias multimodales para una estimación precisa del estado de entrenamiento y retroalimentación.

Palabras clave:

mecanismo de atención análisis de convergencia dinámica multimodal redes neuronales recurrentes modelado de secuencias estimación de estado

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Área de la Ciencia:

Ciencias del Deporte
Inteligencia Artificial
Ingeniería Biomecánica

Sus antecedentes:

El entrenamiento físico personalizado requiere un modelado preciso de datos cinemáticos y fisiológicos complejos.
Los métodos actuales enfrentan desafíos con la inestabilidad de la convergencia y el desajuste de características en el modelado de secuencias multimodales.

Objetivo del estudio:

Desarrollar un marco dinámico para el entrenamiento físico de voleibol cientificado y personalizado.
Abordar la inestabilidad de la convergencia y el desajuste de características en el modelado de secuencias multimodales.

Principales métodos:

Propuso una red de memoria a largo plazo de doble flujo (DS-LSTM) integrada con un mecanismo de atención temporal.
El marco desacopla el aprendizaje de características heterogéneas y optimiza la distribución del peso temporal para secuencias multimodales.

Principales resultados:

Reducción del error de modelado de carga al 3,8% en la estimación de estados de movimiento complejos.
Logró una precisión de clasificación de movimiento del 93,1% y un coeficiente de determinación de ajuste de la trayectoria de velocidad de 0,91.
Demostró una desviación de la trayectoria de velocidad máxima de 0,05 m/s.

Conclusiones:

El DS-LSTM basado en atención optimiza eficazmente el modelado de secuencias multimodales para la estimación del estado de entrenamiento.
El marco propuesto mejora la personalización y cientificación del entrenamiento físico de voleibol.
Validó la efectividad en la estimación de estados de movimiento complejos y sistemas de retroalimentación.