A Neural Network Design Methodology for Structural Control

1997 ◽  
Author(s):  
S. M. Yang ◽  
G. S. Lee
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Adaptive Learning ◽  
Design Methodology ◽  
Learning Rule ◽  
Smart Structure ◽  
Learning Rate ◽  
Design Parameters ◽  
Adaptive Learning Rate ◽  
Propagation Network

Abstract One of the major difficulties in neural network applications is the selection of the parameters in network configuration and the coefficients in learning rule for fast convergence as well as best system performance. This paper developed a network design methodology so that the optimal design parameters/coefficients can be determined in a systematic way thereby avoiding the lengthy trial-and-error. The methodology combines the Taguchi method of quality engineering and the back-propagation network with an adaptive learning rate for their advantages in implementation feasibility and performance robustness. Vibration suppression experiments of a composite smart structure with embedded piezoelectric sensor/actuator validate that the methodology provides an efficient neural controller design, including the plant order, the number of hidden layer neurons, the number of training patterns, and the coefficients of adaptive learning rate.

Neural Network Design by Using Taguchi Method

1999 ◽  
Vol 121 (3) ◽  
pp. 560-563 ◽  
Author(s):  
S. M. Yang ◽  
G. S. Lee
Keyword(s):  
Neural Network ◽  
Taguchi Method ◽  
Network Design ◽  
Adaptive Learning ◽  
Vibration Suppression ◽  
Back Propagation ◽  
Learning Rule ◽  
Design Parameters ◽  
Training Time ◽  
Propagation Network

One of the major difficulties in neural network applications is the selection of the parameters in network configuration and the coefficients in learning rule for fast convergence. This paper develops a network design by combining the Taguchi method and the back-propagation network with an adaptive learning rate for minimum training time and effective vibration suppression. Analyses and experiments show that the optimal design parameters can be determined in a systematic way thereby avoiding the lengthy trial-and-error.

scholarly journals Stability Analysis of Neural Networks-Based System Identification

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Talel Korkobi ◽  
Mohamed Djemel ◽  
Mohamed Chtourou
Keyword(s):  
Stability Analysis ◽  
Adaptive Learning ◽  
Learning Rule ◽  
Stability Domain ◽  
Learning Rate ◽  
Training Algorithm ◽  
Adaptive Learning Rate ◽  
Systems Identification ◽  
The Stability ◽  
Convergence Stability

This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

scholarly journals Analisis Error dan Epoch dengan Pengembangan Adaptive Learning Rate dan Parameter Momentum pada Metode Backpropagation

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Zahratul Fitri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Rate ◽  
Backpropagation Algorithm ◽  
Adaptive Learning Rate ◽  
Best Value ◽  
Artificial Neural Network Ann ◽  
Parameter Values ◽  
Value Of Learning

Abstrak— Algoritma backpropagation merupakan bagian dari Jaringan Syaraf Tiruan (JST) yang memiliki beberapa layar tersembunyi. Algoritma backpropagation juga merupakan multi-layer yang banyak digunakan untuk menyelesaikan persoalan yang luas, akan tetapi, algoritma backpropagation juga memiliki kelamahan pada proses pembelajaran yang cukup lambat. Pada penelitian ini penulis menganalisis bagaimana mengembangkan algoritma backpropagation dengan menggunakan learning rate dan parameter momentum untuk meminimalisir error dan epoch yang akurat sebagai proses menghitung perubahan bobot. Dari hasil penelitian diperoleh bahwa pengembangan yang dilakukan memperoleh nilai paling baik pada nilai momentum yaitu 0,9 dan 1.0 dan nilai learning rate yaitu > 0,7. Hal ini membuktikan bahwa nilai pembelajaran dengan menggunakan nilai parameter momentum dan nilai learning rate diatas sangat baik digunakan sebagai percepatan laju konvergensi.Kata kunci— Algoritma Backpropagation, Parameter Momentum, Adaptive Learning Rate . Abstract— Backpropagation algorithm is part of an Artificial Neural Network (ANN), which has some hidden screen. Backpropagation algorithm is also a multi-layer finish that is widely used for large problems, however, the backpropagation algorithm also has weaknesses in the learning process is quite slow. In this study the authors analyze how to develop a backpropagation algorithm using learning rate and momentum parameters to minimize the error and accurate epoch as the process of calculating the weight change. The result showed that the development is carried out to obtain best value on the momentum value of 0.9 and 1.0 and the value of learning rate is> 0.7. It is proved that the value of learning by using the parameter values of momentum and learning rate values above are best used as a convergence rate acceleration.Keywords— Backpropagation Algorithm, Parameter of Momentum, Adaptive   Learning Rate

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