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Related Concept Videos

Blind Procedures02:07

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Second Order systems II01:18

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
If  ζ...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

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Related Experiment Video

Updated: Jul 7, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization.

S Abrar1, A Zerguine, M Bettayeb

  • 1Dept. of Comput. Eng., King Fahd Univ. of Pet. and Miner., Dhahran, Saudi Arabia.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

A new recursive least squares (RLS)-based algorithm enhances complex-valued backpropagation for stop-and-go decision-directed (S-and-G-DD) blind equalization. This method achieves faster convergence than traditional approaches for data communication systems.

Related Experiment Videos

Last Updated: Jul 7, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

  • Signal Processing
  • Machine Learning
  • Telecommunications

Background:

  • Stop-and-go decision-directed (S-and-G-DD) equalization is a fundamental blind equalization (BE) technique.
  • While effective for mitigating intersymbol interference, S-and-G-DD methods, including neural network applications, often suffer from slow convergence.
  • Existing complex-valued multilayer feedforward neural networks offer robustness but require faster learning for practical data communication.

Purpose of the Study:

  • To develop a novel blind equalization algorithm that addresses the slow convergence issue of S-and-G-DD methods.
  • To improve the initial convergence speed of complex-valued neural network-based blind equalization.
  • To enhance the efficiency of data communication systems by optimizing equalization techniques.

Main Methods:

  • Derivation of a recursive least squares (RLS)-based complex-valued backpropagation learning algorithm.
  • Application of the derived algorithm to S-and-G-DD blind equalization.
  • Comparative analysis through simulations to evaluate convergence performance.

Main Results:

  • The proposed RLS-based complex-valued backpropagation algorithm demonstrates significantly faster initial convergence.
  • The new algorithm maintains the robustness associated with S-and-G-DD methods.
  • Simulation results validate the effectiveness of the algorithm in accelerating the convergence process.

Conclusions:

  • The RLS-based complex-valued backpropagation algorithm offers a superior solution for S-and-G-DD blind equalization.
  • This advancement addresses the critical challenge of slow convergence in complex-valued neural network equalization.
  • The proposed method holds promise for improving the performance of modern data communication systems.