New cascade-correlation growing deep learning neural network algorithm

作     者：Mohamed, Soha Abd El-Moamen Mohamed, Marghany Hassan Farghally, Mohammed F. 

作者机构：Information System Department Faculty of Computer and Information Assiut University Assiut71515 Egypt Computer Science Department Faculty of Computer and Information Assiut University Assiut71515 Egypt 

出 版 物：《Algorithms》 (Algorithms)

年 卷 期：2021年第14卷第5期

核心收录：

主　　题：Learning algorithms 

摘      要：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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.