HarikrishnanNB/stochastic_resonance_and_nl
Stochastic Resonance in Neurochaos Learning
This project helps researchers and engineers in fields like signal processing or neuroscience who are working with noisy data and need to improve classification accuracy. It takes in raw, potentially noisy, signal data and processes it using principles of neurochaos learning and stochastic resonance. The output is a more robust and accurate classification of that data, even when the original signals are weak or unclear. This is designed for those who develop and implement machine learning models for complex, real-world signals.
No commits in the last 6 months.
Use this if you are a researcher or engineer looking to leverage chaotic dynamics and controlled noise to enhance the performance of your classification models on inherently noisy datasets.
Not ideal if you are looking for a plug-and-play solution for common classification tasks without delving into the underlying mathematical and theoretical concepts of neurochaos and stochastic resonance.
Stars
18
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
May 16, 2021
Commits (30d)
0
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