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Related Concept Videos

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.
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
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.
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Classification of Signals01:30

Classification of Signals

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Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

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Related Experiment Videos

Nonlinear channel equalization for QAM signal constellation using artificial neural networks.

J C Patra1, R N Pal, R Baliarsingh

  • 1Dept. of Appl. Electron., Regional Eng. Coll., Rourkela.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
Summary

A novel functional link artificial neural network (FLANN) effectively performs adaptive channel equalization for digital communication systems. This computationally efficient method surpasses traditional equalizers in handling nonlinear channels.

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Area of Science:

  • Digital Communications
  • Artificial Intelligence
  • Signal Processing

Background:

  • Adaptive channel equalization is crucial for reliable digital communication.
  • Artificial neural networks (ANNs) offer potential for complex equalization tasks.
  • Existing methods like LMS may struggle with nonlinear channel distortions.

Purpose of the Study:

  • To propose and evaluate a novel, computationally efficient single-layer functional link artificial neural network (FLANN) for adaptive channel equalization.
  • To investigate the FLANN's capability in handling nonlinear channel equalization for 4-QAM systems.
  • To compare the FLANN's performance against other ANNs and conventional equalizers.

Main Methods:

  • A single-layer functional link artificial neural network (FLANN) was designed, introducing nonlinearity via trigonometric polynomial expansion.
  • The FLANN was applied to channel equalization, treating it as a nonlinear classification problem.
  • Performance was evaluated against multilayer perceptron (MLP), polynomial perceptron network (PPN), and linear LMS equalizers under various channel models.

Main Results:

  • The FLANN demonstrated effective nonlinear channel equalization capabilities.
  • Performance was compared across different ANN structures and channel models.
  • The impact of the eigenvalue ratio (EVR) on equalizer performance was analyzed.
  • Computational complexity of the ANN structures was assessed.

Conclusions:

  • The proposed FLANN provides an efficient and effective solution for adaptive channel equalization, particularly in nonlinear scenarios.
  • FLANN's ability to form nonlinear decision boundaries makes it suitable for complex communication channels.
  • The study offers insights into ANN-based equalization strategies and their comparative performance.