Foundations of Wavelet Networks and Applications

2018 ◽  
Author(s):  
S. Sitharama Iyengar ◽  
S. Sitharama Iyengar ◽  
V.V. Phoha
Keyword(s):  
Wavelet Networks

PYRAMIDAL HYBRID APPROACH: WAVELET NETWORK WITH OLS ALGORITHM-BASED IMAGE CLASSIFICATION

2011 ◽  
Vol 09 (01) ◽  
pp. 111-130 ◽  
Author(s):  
OLFA JEMAI ◽  
MOURAD ZAIED ◽  
CHOKRI BEN AMAR ◽  
MOHAMED ADEL ALIMI
Keyword(s):  
Least Squares ◽  
Image Classification ◽  
Network Architecture ◽  
Hybrid Approach ◽  
Learning Ability ◽  
Wavelet Network ◽  
Wavelet Networks ◽  
Orthogonal Least Squares ◽  
Colour Image ◽  
Beta Wavelet

Taking advantage of both the scaling property of wavelets and the high learning ability of neural networks, wavelet networks have recently emerged as a powerful tool in many applications in the field of signal processing such as data compression, function approximation as well as image recognition and classification. A novel wavelet network-based method for image classification is presented in this paper. The method combines the Orthogonal Least Squares algorithm (OLS) with the Pyramidal Beta Wavelet Network architecture (PBWN). First, the structure of the Pyramidal Beta Wavelet Network is proposed and the OLS method is used to design it by presetting the widths of the hidden units in PBWN. Then, to enhance the performance of the obtained PBWN, a novel learning algorithm based on orthogonal least squares and frames theory is proposed, in which we use OLS to select the hidden nodes. In the simulation part, the proposed method is employed to classify colour images. Comparisons with some typical wavelet networks are presented and discussed. Simulations also show that the PBWN-orthogonal least squares (PBWN-OLS) algorithm, which combines PBWN with the OLS algorithm, results in better performance for colour image classification.

Active control of flexible one-link manipulators using wavelet networks

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1275-1283 ◽  
Author(s):  
V. I. Gervini ◽  
E. M. Hemerly ◽  
S. C. P. Gomes
Keyword(s):  
Dead Zone ◽  
Flexible Manipulator ◽  
Friction Compensation ◽  
Control Law ◽  
Wavelet Network ◽  
Stick Slip ◽  
Control Laws ◽  
Operational Conditions ◽  
Structure Control ◽  
Wavelet Networks

SUMMARYThe design of control laws for flexible manipulators is known to be a challenging problem, when using a conventional actuator, i.e., a motor with gear. This is due to the friction of the nonlinear actuator, which causes torque dead zone and stick-slip behavior, thereby hampering the good performance of the control system. The torque needed to attenuate the vibrations, although calculated by the control law, is consumed by the friction inside the actuator, rendering it ineffective to the flexible structure control. Nonlinear friction varies with different operational conditions of the actuator and a friction compensation mechanism based on these models cannot always keep a good performance. This study proposes a new control strategy using wavelet network to friction compensation. Experimental results obtained with a flexible manipulator attest to the good performance of the proposed control law.

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