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Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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State Space Representation01:27

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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.
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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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...
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Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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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.
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Related Experiment Videos

Probabilistic, Recurrent, Fuzzy Neural Network for Processing Noisy Time-Series Data.

Yong Li, Richard Gault, T Martin McGinnity

    IEEE Transactions on Neural Networks and Learning Systems
    |March 9, 2021
    PubMed
    Summary
    This summary is machine-generated.

    A new recurrent probabilistic fuzzy neural network (PFNN-R) effectively handles noisy data. This algorithm enhances big data analytics by improving time-series modeling with greater accuracy and robustness.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Big data analytics requires algorithms robust to incomplete or noisy data.
    • Recurrency in signal processing enhances accuracy, particularly in biological signals.
    • Existing probabilistic fuzzy neural networks (PFNN) address noise but lack recurrent feedback.

    Purpose of the Study:

    • To propose a novel probabilistic fuzzy neural algorithm with recurrent feedback (PFNN-R).
    • To enhance the capability of PFNNs to accommodate diverse types of noisy data.
    • To develop a mechanism for shaping the probabilistic density function of fuzzy membership.

    Main Methods:

    • Introduction of a recurrent probabilistic generation module into PFNN architecture (PFNN-R).
    • Development of a back-propagation-based mechanism for fuzzy membership distribution shaping.
    • Application of the PFNN-R algorithm to benchmark problems for performance evaluation.

    Main Results:

    • The proposed PFNN-R algorithm demonstrates enhanced ability to accommodate noisy data.
    • Simulation results confirm the advancement of PFNNs in modeling time-series data with high-intensity, random noise.
    • The recurrent feedback mechanism improves robustness and accuracy in signal processing tasks.

    Conclusions:

    • The PFNN-R algorithm offers a significant advancement for big data analytics and signal processing.
    • The integration of recurrency and probabilistic fuzzy logic provides a powerful tool for handling uncertain and noisy data.
    • This approach extends the applicability of PFNNs to complex time-series modeling challenges.