Application of the Givens Rotations in the Neural Network Learning Algorithm

2016 ◽  
pp. 46-56 ◽  
Author(s):  
Jarosław Bilski ◽  
Bartosz Kowalczyk ◽  
Jacek M. Żurada
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network ◽  
Givens Rotations

Generalization of the backpropagation neural network learning algorithm to permit complex weights

1990 ◽  
Vol 29 (11) ◽  
pp. 1591 ◽  
Author(s):  
Gordon R. Little ◽  
Steven C. Gustafson ◽  
Robert A. Senn
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Backpropagation Neural Network ◽  
Neural Network Learning ◽  
Network Learning

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

scholarly journals Kernel-blending connection approximated by a neural network for image classification

2020 ◽  
Vol 6 (4) ◽  
pp. 467-476
Author(s):  
Xinxin Liu ◽  
Yunfeng Zhang ◽  
Fangxun Bao ◽  
Kai Shao ◽  
Ziyi Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Function Approximation ◽  
Feature Space ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hinge Loss ◽  
The Neural Network ◽  
Good Classification ◽  
Kernel Mapping

AbstractThis paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classification through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classifier is used as an output layer of the neural network to provide classification results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.

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