On‐line self‐learning PID based PSS using self‐recurrent wavelet neural network identifier and chaotic optimization

2012 ◽  
Vol 31 (6) ◽  
pp. 1872-1891 ◽  
Author(s):  
Soheil Ganjefar ◽  
Mojtaba Alizadeh
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
On Line ◽  
Self Learning

A Self-Constructing Wavelet Neural Network Controller to Mitigate the Subsynchronous Oscillations

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Mohsen Farahani ◽  
Soheil Ganjefar
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Adaptive Learning ◽  
Control Process ◽  
Wavelet Neural Network ◽  
Intelligent Control System ◽  
Intelligent Technique ◽  
Neural Network Controller ◽  
Learning Rates ◽  
On Line

This study proposes a new intelligent controller based on self-constructing wavelet neural network (SCWNN) to suppress the subsynchronous resonance (SSR) in power systems compensated by series capacitors. In power systems, the use of intelligent technique is inevitable, because of the uncertainties such as operating condition variations, different kinds of disturbances, etc. Accordingly, an intelligent control system that is an on-line trained SCWNN controller with adaptive learning rates is used to mitigate the SSR. The Lyapunov stability method is used to extract the adaptive learning rates. Hence, the convergence of the proposed controller can be guaranteed. At first, there is no wavelet in the structure of controller. They are automatically generated and begin to grow during the control process. In the whole design process, the identification of the controlled plant dynamic is not necessary according to the ability of the proposed controller. The effectiveness and robustness of the proposed controller are demonstrated by using the simulation results.

Study of a New Wavelet Neural Network of Image Compression Simulation

2012 ◽  
Vol 490-495 ◽  
pp. 623-627
Author(s):  
Xue Zhang Zhao ◽  
Qun Qi
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Bp Neural Network ◽  
Local Area ◽  
Wavelet Neural Network ◽  
Convergence Speed ◽  
Good Time ◽  
Time Frequency ◽  
Hidden Layer ◽  
Self Learning

In the practical need in order to make the most effective image compression in this paper, a new image compression used wavelet neural network model, and gives the corresponding calculation formula and algorithm procedures, By using wavelet transform good time-frequency local area on the characteristics and neural network self-learning function characteristics, overcome traditional BP neural network of hidden-layer points are difficult to be determined and the convergence speed is slow and easy to converge to a local minimum points shortcomings. The results of the simulation experiment prove wavelet neural network image compression characteristic and the convergence speed are much better than traditional BP neural network, and show that the algorithm is effective and feasible.

On-Line Handy Handwriting Chinese Characters Input for Non-Chinese Speakers Based on Wavelet Neural Network

2016 ◽  
Vol 30 (06) ◽  
pp. 1659017 ◽  
Author(s):  
Tongcheng Huang ◽  
Siyang Zhang ◽  
Xu Duan ◽  
Ronglong Liang
Keyword(s):  
Neural Network ◽  
Genetic Code ◽  
Wavelet Neural Network ◽  
Chinese Characters ◽  
Input Information ◽  
Chinese Speakers ◽  
On Line ◽  
Hidden Layer ◽  
Online Handwriting

Non-Chinese speakers hold increasing opportunities and need to process Chinese information and communicate in Chinese. This paper, with the purpose of facilitating the handwriting input of Chinese characters for non-Chinese speakers, is directed towards the development of the handwriting rules and vocabulary for Latin-style anti-cursive characters and the ways of their selection and classification. This aims to build a practical platform by utilizing three characteristics of wavelet neural network — automatically ascertaining the number of hidden layer unit, converging rapidly and never running into the partial minimum of networks — for a simple Latin-style online handwriting input and processing, meanwhile, taking the customary handwriting habits of non-Chinese speakers. The paper, based on profound information of cursive characters, deciphered the genetic code of ancient cursive symbols and made clear the rules for characters changing into its cursive style. As a result, it breaks the bottleneck, which enables non-Chinese speakers to easily input information through handwriting Chinese characters.

The Slope Deformation Forecast Model Based on Kalman Filter and Wavelet Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 323-327
Author(s):  
Bing Jun Shi ◽  
Yong Fen Ruan ◽  
Qi Li ◽  
Yong Hong Wu
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Geotechnical Engineering ◽  
Forecast Model ◽  
Wavelet Neural Network ◽  
Slope Deformation ◽  
Time Frequency ◽  
Learning Function ◽  
Time Frequency Localization ◽  
Self Learning

Deformation is the macroscopic index for the structure of geotechnical engineering, it is important for the design and construction of geotechnical engineering to monitor the deformation and analyze the monitored data. Kalman filter can enhance the effectiveness of the monitored data and wavelet neural network has the favorable time－frequency localization features and self－learning function. Firstly, the monitored data has been filtered by Kalman filter, and then a deformation forecast model will be established by means of combining with neural network wavelet to predict the deformation of actual engineering. The result shows that the forecast model is successful and effective to forecast the slope deformation.

scholarly journals Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network

2014 ◽  
Vol 10 (2) ◽  
pp. 118-129
Author(s):  
Adel Obed ◽  
Ameer Saleh
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Motor Drive ◽  
Bldc Motor ◽  
Wide Range ◽  
Stator Resistance ◽  
And Control ◽  
Self Learning

In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.

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