Hybrid Learning Algorithms for Feed-Forward Neural Networks

1994 ◽  
pp. 61-68 ◽  
Author(s):  
Marcus Pfister ◽  
Raúl Rojas
Keyword(s):  
Neural Networks ◽  
Learning Algorithms ◽  
Hybrid Learning ◽  
Feed Forward ◽  
Feed Forward Neural Networks

A New Type of Hybrid Learning Algorithm for Three-Layered Feed-Forward Neural Networks

2014 ◽  
Vol 1030-1032 ◽  
pp. 1627-1632
Author(s):  
Yun Jun Yu ◽  
Sui Peng ◽  
Zhi Chuan Wu ◽  
Peng Liang He
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Least Square Method ◽  
Hybrid Learning ◽  
Least Square ◽  
Feed Forward ◽  
Hybrid Learning Algorithm ◽  
Layer Function ◽  
Feed Forward Neural Networks ◽  
New Type

The problem of local minimum cannot be avoided when it comes to nonlinear optimization in the learning algorithm of neural network parameters, and the larger the optimization space is, the more obvious the problem becomes. This paper proposes a new type of hybrid learning algorithm for three-layered feed-forward neural networks. This algorithm is based on three-layered feed-forward neural networks with output layer function, namely linear function, combining a quasi Newton algorithm with adaptive decoupled step and momentum (QNADSM) and iterative least square method to export. Simulation proves that this hybrid algorithm has strong self-adaptive capability, small calculation amount and fast convergence speed. It is an effective engineer practical algorithm.

scholarly journals An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends

2020 ◽  
Author(s):  
Muhammad Haseeb Arshad ◽  
M. A. Abido
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Learning Algorithms ◽  
Neural Nets ◽  
Sequential Learning ◽  
Future Trends ◽  
Feed Forward ◽  
Training Time ◽  
Feed Forward Neural Networks ◽  
Hidden Layer

This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.

scholarly journals An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends

2020 ◽  
Author(s):  
Muhammad Haseeb Arshad ◽  
M. A. Abido
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Learning Algorithms ◽  
Neural Nets ◽  
Sequential Learning ◽  
Future Trends ◽  
Feed Forward ◽  
Training Time ◽  
Feed Forward Neural Networks ◽  
Hidden Layer

This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.

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