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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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.
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Related Experiment Videos

Optimal neural network algorithm for on-line string matching.

Y W Leung1, J S Zhang, Z B Xu

  • 1Dept. of Comput. Sci., Hong Kong Baptist Univ., Kowloon.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network algorithm for the online string matching problem. The parallel algorithm efficiently finds pattern occurrences in streaming text data using McCulloch-Pitts neurons.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • The online string matching problem involves finding a pattern within a text where the text is processed sequentially.
  • Existing algorithms may face limitations in terms of speed and resource utilization for streaming data.
  • Parallel processing offers potential for enhanced efficiency in computational tasks.

Purpose of the Study:

  • To develop a space-time optimal parallel algorithm for the online string matching problem.
  • To leverage a neural network approach for efficient text processing.
  • To achieve real-time pattern detection in streaming text data.

Main Methods:

  • A parallel algorithm utilizing McCulloch-Pitts neurons arranged in a linear array was designed.
  • The algorithm processes each character of the input text in a single step.
  • The neural network architecture is specifically tailored for sequential text input.

Main Results:

  • The proposed algorithm achieves space-time optimality for the online string matching problem.
  • Each text character is processed in one step, leading to efficient computation.
  • The algorithm requires at most n iteration steps for a text of length n.

Conclusions:

  • The neural network-based parallel algorithm provides an efficient solution for online string matching.
  • This approach demonstrates the effectiveness of neural networks in solving complex string processing problems.
  • The algorithm offers a significant advancement in real-time pattern detection for streaming data.