RADIAL BASIS NEURAL NETWORK WITH MULTIPLE CONNECTED WEIGHTS
Nashr sanasi
25.04.2026
Jurnal
Sun'iy intellektni pedagogik ta'limga tadbiq etishning ustivor yo'nalishlari
Nashr
Sun'iy intellektni pedagogik ta'limga tadbiq etishning ustivor yo'nalishlari
Sahifalar
344-350
Mualliflar
Annotatsiya
In this work, we propose a new type of radial basis neural network model where the connection between two units is not a single value but a set of values, which means multi-connected weights exist. In our model, each pair of units is connected by more than one link. These links mimic different neurotransmitters, and their number reflects the number of neurotransmitter types considered. Experimental tests on benchmark datasets from the Machine Learning Repository show that using radial basis with multiple weight connections improves performance over traditional neural networks. This method gives a new way to design and build artificial neural networks.
Kalit so‘zlar
classification
radial basis function
radial basis neural network
neurotransmitter
multiple connections
weight
hidden layer
Foydalanilgan adabiyotlar
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