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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Veröffentlicht in:Algorithms Jg. 14; H. 5; S. 158
Hauptverfasser: Mohamed, Soha Abd El-Moamen, Mohamed, Marghany Hassan, Farghally, Mohammed F.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.05.2021
Schlagworte:
Algorithms
Artificial neural networks
cascade correlation
Classification
Computer architecture
constructive neural networks
Correlation
Deep learning
Machine learning
Neural networks
Neurons
ISSN:1999-4893, 1999-4893
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1999-4893
1999-4893
DOI:10.3390/a14050158