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Title: | Nonsynaptic Backpropagation Learning of Interval-valued Long-term Cognitive Networks | Authors: | FRIAS DOMINGUEZ, Mabel NAPOLES RUIZ, Gonzalo Filiberto, Yaima Bello, Rafael VANHOOF, Koen |
Issue Date: | 2021 | Publisher: | IEEE | Source: | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), IEEE, | Series/Report: | IEEE International Joint Conference on Neural Networks (IJCNN) | 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. | Notes: | Frias, M (corresponding author), Univ Camaguey, Dept Comp Sci, Camaguey, Cuba. mabel.frias@reduc.edu.cu; napoles.gonzalo@gmail.com; yaima.filiberto@amvsoluciones.com; rbellop@uclv.edu.cu; koen.vanhoof@uhasselt.be |
Keywords: | long-term interval cognitive networks;interval sets;nonsynaptic learning | Document URI: | http://hdl.handle.net/1942/36564 | ISBN: | 978-0-7381-3366-9 | DOI: | 10.1109/IJCNN52387.2021.9533586 | ISI #: | WOS:000722581702047 | Rights: | 2021 IEEE | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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