Nonsynaptic Backpropagation Learning of Interval-valued Long-term Cognitive Networks
Frias,Mabel Napoles,Gonzalo Vanhoof,Koen Filiberto,Yaima Bello,Rafael
Frias,Mabel
Napoles,Gonzalo
Vanhoof,Koen
Filiberto,Yaima
Bello,Rafael
Abstract
This paper elaborates on the modeling and simulation of complex systems involving uncertainty. More explicitly, we are interested in situations in which experts hesitate about the exact values of variables when designing the model. Such situations can be modeled using Interval-valued Long-term Cognitive Networks (IVLTCNs). In this model, the activation values and the weights between neural concepts are expressed as interval grey numbers. Unlike other grey cognitive networks, our model neither imposes restrictions on the weights nor performs a whitenization process. The second contribution of this paper is a nonsynaptic grey backpropagation algorithm, which allows adjusting the learnable parameters of IVLTCNs under uncertainty conditions. Moreover, this learning algorithm does not alter the linear knowledge representations provided by domain experts during the modeling phase.
Description
Publisher Copyright: © 2021 IEEE.
Date
2021-07-18
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Publisher
Institute of Electrical and Electronics Engineers Inc.
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Keywords
interval sets, long-term interval cognitive networks, nonsynaptic learning
Citation
Frias, M, Napoles, G, Vanhoof, K, Filiberto, Y & Bello, R 2021, Nonsynaptic Backpropagation Learning of Interval-valued Long-term Cognitive Networks. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Shenzhen, China, 18/07/21. https://doi.org/10.1109/IJCNN52387.2021.9533586