https://github.com/artemik/fuzzy-neural-network-tsk

Fuzzy Neural Network TSK (Takagi-Sugeno-Kang) with hybrid training and C-Means clustering.
https://github.com/artemik/fuzzy-neural-network-tsk

cmeans fuzzy hybrid-training neural-network takagi-sugeno-kang tsk

Last synced: about 1 year ago
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Fuzzy Neural Network TSK (Takagi-Sugeno-Kang) with hybrid training and C-Means clustering.

• Host: GitHub
• URL: https://github.com/artemik/fuzzy-neural-network-tsk
• Owner: artemik
• License: mit
• Created: 2017-08-04T15:36:50.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2017-08-04T17:22:29.000Z (almost 9 years ago)
• Last Synced: 2025-02-28T05:56:17.960Z (over 1 year ago)
• Topics: cmeans, fuzzy, hybrid-training, neural-network, takagi-sugeno-kang, tsk
• Language: Groovy
• Homepage:
• Size: 60.5 KB
• Stars: 7
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Fuzzy Neural Network TSK (Takagi-Sugeno-Kang)

Hybrid Training algorithm and C-Means for initial Gaussian function parameters setup.

In this example the neural network is trained to forecast a currency rate.

Implemented in Groovy (slow, but fun).