scholarly journals A coded block neural network system suitable for VLSI implementation using an adaptive learning-rate epoch-based back propagation technique

2002 ◽  
Author(s):  
M.W. Mao ◽  
B.Y. Chen ◽  
J.B. Kuo
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Back Propagation ◽  
Learning Rate ◽  
Vlsi Implementation ◽  
Network System ◽  
Adaptive Learning Rate ◽  
Neural Network System ◽  
Propagation Technique

NEUROSLAB – neural network system for horizontal formwork selection

1995 ◽  
Vol 22 (4) ◽  
pp. 785-792 ◽  
Author(s):  
Awad S. Hanna ◽  
Ahmed B. Senouci
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Back Propagation ◽  
Network System ◽  
Neural Network System ◽  
Testing Error ◽  
Training Error ◽  
Training Examples ◽  
High Level ◽  
Selection Of

This paper presents an overview of the neural network technique as a tool for concrete formwork selection. The paper discusses the development and the implementation of a neural network system, NEUROSLAB, for the selection of horizontal formwork systems. A rule-based expert system for the selection of horizontal systems, SLABFORM, was used as the basis for the development of NEUROSLAB. A training set of 202 cases was used to train the network. The network adequately learned the training examples with an average training error of 0.025. A set of 50 cases was used to test the generalization ability of the system. The network was able to accurately select the appropriate horizontal formwork system with an average testing error of 0.057. The ability of the network to deal with noisy data was also tested. Up to 50% noise was added to the data and introduced to the network. The results showed that the network presented could accurately identify the appropriate horizontal formwork system at high level of noise. Finally, the solution chosen by an expert was compared to that produced by the network. The network was able to mimic the expert's formwork selection. Key words: formwork, horizontal formwork systems, neural network, formwork selection, back propagation, expert system.

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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