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Construction and Supervised Learning of Long-Term Grey Cognitive Networks.
IEEE Transactions on Cybernetics
|May 21, 2019
Summary
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.
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.
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