A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm
In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural ne...
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| Published in | Algorithms Vol. 14; no. 5; p. 158 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.05.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1999-4893 1999-4893 |
| DOI | 10.3390/a14050158 |
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| Abstract | In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it. |
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| AbstractList | In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it. |
| Author | Farghally, Mohammed F. Mohamed, Soha Abd El-Moamen Mohamed, Marghany Hassan |
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| Cites_doi | 10.1007/s10710-017-9314-z 10.1016/0893-6080(94)90061-2 10.1142/S0129065794000074 10.1016/0893-6080(91)90032-Z 10.1080/01621459.1981.10477729 10.1109/72.363426 10.1080/09540098908915647 10.1007/978-3-642-79119-2_7 10.1007/978-3-642-04512-7_1 10.1007/978-3-642-04512-7_12 10.1186/s12885-017-3877-1 10.1109/TNNLS.2019.2918225 10.1109/72.207612 10.1049/ip-cta:19951969 10.1155/2014/781670 10.1016/j.neucom.2017.08.040 10.1016/0893-6080(94)90058-2 10.1007/978-1-4419-9226-0_5 10.1088/0305-4470/22/12/019 10.1118/1.3528204 10.1109/72.839013 10.1007/s00521-019-04196-8 10.1162/neco.1991.3.2.213 10.3390/informatics6010001 10.1109/72.623214 10.1109/72.572102 |
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| SubjectTerms | Algorithms Artificial neural networks cascade correlation Classification Computer architecture constructive neural networks Correlation Deep learning Machine learning Neural networks Neurons |
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| Title | A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm |
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