A fast learning algorithm for neural networks and its applications to adaptive equalizers

1994 ◽  
Vol 77 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Teruyuki Miyajima ◽  
Takaaki Hasegawa ◽  
Misao Haneishi
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Fast Learning ◽  
Adaptive Equalizers

A fast learning algorithm for time-delay neural networks

2002 ◽  
Vol 148 (1-4) ◽  
pp. 27-39 ◽  
Author(s):  
Minghu Jiang ◽  
Georges Gielen ◽  
Beixing Deng ◽  
Xiaoyan Zhu
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Learning Algorithm ◽  
Fast Learning

Structure Impact Localization Using Emerging Artificial Intelligence Algorithms

2015 ◽  
pp. 103-123
Author(s):  
Qingsong Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Back Propagation ◽  
Damage Localization ◽  
Localization Accuracy ◽  
Fast Learning ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks. In theory, this algorithm is able to provide good generalization capability at extremely fast learning speed. Comparative studies of benchmark function approximation problems revealed that ELM can learn thousands of times faster than conventional neural network (NN) and can produce good generalization performance in most cases. Unfortunately, the research on damage localization using ELM is limited in the literature. In this chapter, the ELM is extended to the domain of damage localization of plate structures. Its effectiveness in comparison with typical neural networks such as back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) is illustrated through experimental studies. Comparative investigations in terms of learning time and localization accuracy are carried out in detail. It is shown that ELM paves a new way in the domain of plate structure health monitoring. Both advantages and disadvantages of using ELM are discussed.

scholarly journals Analysis of the ReSuMe Learning Process For Spiking Neural Networks

2008 ◽  
Vol 18 (2) ◽  
pp. 117-127 ◽  
Author(s):  
Filip Ponulak
Keyword(s):  
Neural Networks ◽  
Learning Process ◽  
Learning Algorithm ◽  
Real Life ◽  
Optimal Solution ◽  
Spiking Neural Networks ◽  
Fast Learning ◽  
Life Tasks ◽  
The Neural Networks ◽  
The Stability

Analysis of the ReSuMe Learning Process For Spiking Neural NetworksIn this paper we perform an analysis of the learning process with the ReSuMe method and spiking neural networks (Ponulak, 2005; Ponulak, 2006b). We investigate how the particular parameters of the learning algorithm affect the process of learning. We consider the issue of speeding up the adaptation process, while maintaining the stability of the optimal solution. This is an important issue in many real-life tasks where the neural networks are applied and where the fast learning convergence is highly desirable.

Functions of Multi-Layered Neural Networks and their Fast Learning Algorithm

1990 ◽  
Vol 110 (3) ◽  
pp. 141-147
Author(s):  
Kazuo Asakawa ◽  
Nobuo Watanabe ◽  
Akira Kawamura ◽  
Ryusuke Masuoka ◽  
Jun-ichi Tanahashi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Fast Learning

A fast learning algorithm for training feedforward neural networks

2006 ◽  
Vol 37 (10) ◽  
pp. 709-722 ◽  
Author(s):  
Ashok Kumar Goel ◽  
Suresh C. Saxena ◽  
Surekha Bhanot
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Feedforward Neural Networks ◽  
Fast Learning
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