C++ implementation of high-performance associative neural networks models based on pseudo-inverse learning rule, also known as projection rule or attractor-based rule

Features

  • This project is based on the work and PhD thesis of Dmitry O. Gorodnichy and Oleksiy K. Dekhtyarenko
  • Implementation of most known and efficient learning rules for associative Hopfield-like attractor-based neural network

Project Samples

Project Activity

See All Activity >

Follow PINNLib

PINNLib Web Site

Other Useful Business Software
Find Hidden Risks in Windows Task Scheduler Icon
Find Hidden Risks in Windows Task Scheduler

Free diagnostic script reveals configuration issues, error patterns, and security risks. Instant HTML report.

Windows Task Scheduler might be hiding critical failures. Download the free JAMS diagnostic tool to uncover problems before they impact production—get a color-coded risk report with clear remediation steps in minutes.
Download Free Tool
Rate This Project
Login To Rate This Project

User Ratings

★★★★★
★★★★
★★★
★★
1
0
0
0
0
ease 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 4 / 5
features 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 5 / 5
design 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 4 / 5
support 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 2 / 5

User Reviews

  • Very useful library and sample codes to understand and implement associative neural networks, can be used to implement many associative recognition tasks, such as face recognition. The only available free codes for this type of neural networks. Great for students and researchers!
Read more reviews >

Additional Project Details

Registered

2013-12-26