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Construction and Supervised Learning of Long-Term Grey Cognitive Networks.

Gonzalo Napoles, Jose L Salmeron, Koen Vanhoof

IEEE Transactions on Cybernetics

|May 21, 2019

Summary

This summary is machine-generated.

This study introduces long-term grey cognitive networks, enhancing neural models with grey numbers for better knowledge representation. The new model achieves superior accuracy compared to existing methods.

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Area of Science:

Artificial Intelligence
Computational Neuroscience
Machine Learning

Background:

Modeling real-world systems with neural networks faces challenges in knowledge representation and error simulation.
Formalizing knowledge precisely with crisp numbers is often difficult.

Purpose of the Study:

To present long-term grey cognitive networks (LTCNs) that expand upon existing LTCNs by incorporating grey numbers.
To enable embedding knowledge into neural networks using weights and constricted neurons.

Main Methods:

Developed long-term grey cognitive networks integrating grey numbers into LTCNs.
Proposed two network construction procedures for historical data.
Introduced a regularization method with a non-synaptic backpropagation algorithm.

Main Results:

The proposed LTCN model with grey numbers demonstrates improved accuracy.
Outperformed the original LTCN model and other state-of-the-art methods.

Conclusions:

Long-term grey cognitive networks offer a robust approach for modeling systems with imprecise knowledge.
The method enhances accuracy and knowledge embedding in neural networks.