Progressive Operational Perceptrons

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• 作者：Serkan Kiranyaz^a ; ^{mkiranyaz@qu.edu.qa}Author Vitae ; Turker Ince^b ; ^{turker.ince@izmirekonomi.edu.tr}Author Vitae ; Alexandros Iosifidis^c ; ^{alexandros.iosifidis@tut.fi}Author Vitae ; Moncef Gabbouj^c ; ^{moncef.gabbouj@tut.fi}Author Vitae
• 关键词：Artificial neural networks ; Multi-layer perceptrons ; Progressive operational perceptrons ; Diversity ; Scalability
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：8 February 2017
• 年：2017
• 卷：224
• 期：Complete
• 页码：142-154
• 全文大小：2024 K
• 卷排序：224

文摘

To address the limitations and drawbacks of MLP neuron model the generalized model of the biological neurons is proposed. Progressive Operational Perceptrons (POPs) is self adaptive and built progressively (incrementally) just like biological neurons. A POP shares the same properties of a typical MLPs and can be identical to a MLP providing that the MLP operators are used. The best set of operators is searched according to the learning problem at hand and the minimal network is built progressively. With the right blend of non-linear operators, POPs can learn very complex problems that cannot be learnt by deeper MLPs.