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Related Concept Videos

State Space Representation01:27

State Space Representation

232
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...
232
State Space to Transfer Function01:21

State Space to Transfer Function

229
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:
229
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

274
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...
274
Classification of Systems-II01:31

Classification of Systems-II

171
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
171
Transfer Function to State Space01:23

Transfer Function to State Space

294
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...
294
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

123
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
123

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A double-cycle echo state network topology for time series prediction.

Jun Fu1, Guangli Li1, Jianfeng Tang1

  • 1College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China.

Chaos (Woodbury, N.Y.)
|September 11, 2023
PubMed
Summary

A new Double-Cycle Echo State Network (DCESN) improves time series prediction by using fixed weights and simpler connections. This enhances performance, stability, and hardware implementation compared to traditional Echo State Networks (ESN).

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Time Series Analysis

Background:

  • Echo State Networks (ESN) are effective for time series prediction due to their reservoir computing properties.
  • Traditional ESNs suffer from performance instability and high computational cost due to random and complex reservoir connections.
  • Hardware implementation of ESNs is challenging because of their inherent randomness and complexity.

Purpose of the Study:

  • To propose a Double-Cycle Echo State Network (DCESN) to address the limitations of traditional ESNs.
  • To improve prediction performance, stability, and reduce computational time.
  • To simplify hardware implementation for broader ESN applications.

Main Methods:

  • Developed a DCESN based on the Li-ESN model.
  • Implemented fixed weights for improved predictability and stability.
  • Simplified reservoir connections to decrease computational complexity.

Main Results:

  • DCESN demonstrated comparable or superior prediction performance against traditional ESNs across various datasets.
  • The model showed robustness against noise and parameter fluctuations.
  • Reduced computational time and simplified network structure were observed.

Conclusions:

  • DCESN offers a more stable and efficient alternative for time series prediction.
  • The fixed weights and simpler connections facilitate easier hardware implementation.
  • DCESN shows significant potential for future applications in time series modeling.