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المؤلفKiranyaz, Mustafa Serkan
المؤلفMalik J.
المؤلفAbdallah H.B.
المؤلفInce T.
المؤلفIosifidis A.
المؤلفGabbouj M.
تاريخ الإتاحة2022-04-26T12:31:18Z
تاريخ النشر2021
اسم المنشورNeural Networks
المصدرScopus
المعرّفhttp://dx.doi.org/10.1016/j.neunet.2021.02.028
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103972773&doi=10.1016%2fj.neunet.2021.02.028&partnerID=40&md5=aee7a8cc47efef20bdafd78fc4a7b93f
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30586
الملخصOperational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as network homogeneity with the sole linear neuron model. ONNs are heterogeneous networks with a generalized neuron model. However the operator search method in ONNs is not only computationally demanding, but the network heterogeneity is also limited since the same set of operators will then be used for all neurons in each layer. Moreover, the performance of ONNs directly depends on the operator set library used, which introduces a certain risk of performance degradation especially when the optimal operator set required for a particular task is missing from the library. In order to address these issues and achieve an ultimate heterogeneity level to boost the network diversity along with computational efficiency, in this study we propose Self-organized ONNs (Self-ONNs) with generative neurons that can adapt (optimize) the nodal operator of each connection during the training process. Moreover, this ability voids the need of having a fixed operator set library and the prior operator search within the library in order to find the best possible set of operators. We further formulate the training method to back-propagate the error through the operational layers of Self-ONNs. Experimental results over four challenging problems demonstrate the superior learning capability and computational efficiency of Self-ONNs over conventional ONNs and CNNs.
اللغةen
الناشرElsevier Ltd
الموضوعComputational efficiency
Convolution
Efficiency
Heterogeneous networks
Neural networks
Personnel training
Convolutional neural network
Generalized neuron
Generative neuron
Network heterogeneity
Network homogeneity
Neural-networks
Neuron-models
Operational neural network
Search method
Self-organised
Neurons
article
convolutional neural network
human
learning
nerve cell network
machine learning
Machine Learning
العنوانSelf-organized Operational Neural Networks with Generative Neurons
النوعArticle
الصفحات294-308
رقم المجلد140
dc.accessType Abstract Only


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