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Related Concept Videos

The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power flow program computes the...
Equivalent Resistance01:16

Equivalent Resistance

In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
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Bernoulli's Equation: Problem Solving01:16

Bernoulli's Equation: Problem Solving

A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
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Related Experiment Videos

Learning the pseudoinverse solution to network weights.

J Tapson1, A van Schaik

  • 1Bioelectronics and Neuroscience Group, The MARCS Institute, University of Western Sydney, Building XB, Kingswood Campus, Kingswood 2751 NSW, Australia. j.tapson@uws.edu.au

Neural Networks : the Official Journal of the International Neural Network Society
|April 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel, biologically plausible online method for computing pseudoinverses in neural networks, improving upon singular value decomposition for neuromorphic engineering applications.

Keywords:
Biological plausibilityExtreme learning machineMoore–Penrose pseudoinverseNeural engineering

Related Experiment Videos

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Neuromorphic Engineering

Background:

  • Neural networks increasingly use random projection, nonlinear activation, and pseudoinverse for regression/classification.
  • The pseudoinverse computation via singular value decomposition (SVD) lacks biological plausibility and online/adaptive capabilities.

Purpose of the Study:

  • To present a biologically plausible, online method for computing pseudoinverses.
  • To offer an adaptive solution for non-stationary data streams.
  • To improve memory efficiency compared to SVD.

Main Methods:

  • Developed an incremental algorithm for precise pseudoinverse computation.
  • Ensured biological plausibility as a neural network learning method.
  • Designed for adaptability to non-stationary data.

Main Results:

  • The proposed method precisely computes pseudoinverses online.
  • It demonstrates biological plausibility as a learning mechanism.
  • The method is adaptable for non-stationary data streams.
  • It offers significant memory efficiency gains over SVD.

Conclusions:

  • The novel online pseudoinverse computation method is a viable, biologically plausible alternative for neuromorphic engineering.
  • This approach enhances adaptability and memory efficiency in neural network learning.