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.

An intelligent system for selection of grinding wheels

1997 ◽  
Vol 211 (8) ◽  
pp. 635-641 ◽  
Author(s):  
Y Li ◽  
B Mills ◽  
W B Rowe
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Back Propagation ◽  
Grinding Wheel ◽  
Neural Network System ◽  
Grinding Conditions ◽  
Training Examples ◽  
Using Data ◽  
Hidden Layer ◽  
Propagation Network

This paper describes the development of a neural network system for grinding wheel selection. The system employs a back-propagation network with one hidden layer and was trained using data from reference handbooks. It is shown that a neural network is capable of learning the relationship between the wheel and the grinding process without a requirement for rules or equations. It was further found that a relatively small number of training examples allows the system to produce reliable recommendations for a much greater number of combinations of grinding conditions. The system was developed on a PC using the C++ programming language.

Application of Artificial Neural Network for Cotton Boil Spoiling Disease Prediction

2010 ◽  
Vol 143-144 ◽  
pp. 233-237
Author(s):  
Fu Gui Chen ◽  
Bao Jian Zhang ◽  
Jun Hui Fu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Disease Prediction ◽  
Network System ◽  
Neural Network System ◽  
Average Quantity ◽  
Artificial Neural ◽  
High Output

Based on the database of cotton boil spoiling disease in Xinxiang, a computerized intelligent expert system was established by using the Reverse Model of artificial neural network. With its speediness, robustness and 100%predicting accuracy, the system can be used as an effective method to predict the trend of cotton diseases. In recent years, we have seem some reports for which use artificial neural network system to forecast the disease of crops, but the artificial neural network using for predicting cotton boil spoiling disease have not been seen yet. Xinxiang is a city of Henan province of china, according to the survey materials of 10 years, the high output cotton boil spoiling disease break out every 4 years, the average quantity is 1.53, the rate of boil spoiling disease is 11.84%, so the loss is 168.28 . In order to prevent the cotton boil spoiling disease, we should forecast the disease, by doing this, it can increase quantity and quality of the cotton.

Application of Artificial Neural Network for Prediction of Boride Layer Depth Obtained on XC38 Steel in Molten Salts

2015 ◽  
Vol 365 ◽  
pp. 194-199 ◽  
Author(s):  
Karim Rayane ◽  
Omar Allaoui
Keyword(s):  
Neural Network ◽  
Molten Salts ◽  
Good Accuracy ◽  
Back Propagation ◽  
Boride Layer ◽  
Network System ◽  
Back Propagation Algorithm ◽  
Layer Depth ◽  
Silicon Carbides ◽  
Neural Network System

This paper discusses an application of neural network system on the performance of boride layer thickness. Boriding treatment was carried out in three different molten salts consisting of borax (Na2B4O7) added to boron carbide (B4C), aluminum (Al) and silicon carbides (SiC). The substrate used in this study was XC38 steel. Borides layers involved in this work was obtained from a boriding treatment at the temperature range of 800-1050 °C with 50°C interval for 2, 4 and 6 h. A numerical experiment using normalized and binarized values was carried out, using a back-propagation algorithm in ANN. The modeling shows that for the three bath the depth of boride layer was predicted with good accuracy, with a highest performance of normalized values along experimental data range.

scholarly journals A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions

2012 ◽  
pp. 56-61
Author(s):  
Pratibha Rani ◽  
Anshu Sirohi ◽  
Manish Kumar Singh
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Network System ◽  
Morphological Operations ◽  
Gray Level ◽  
Back Propagation Algorithm ◽  
Translation Invariant ◽  
The Neural Network ◽  
Neural Network System ◽  
Level Shifts

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%.

USE OF GRADATED PATTERNS IN AN ASSOCIATIVE NEURAL MEMORY FOR INVARIANT PATTERN RECOGNITION

1994 ◽  
Vol 08 (02) ◽  
pp. 595-607
Author(s):  
KAZUKUNI KOBARA ◽  
TAIHO KANAOKA ◽  
YOSHIHIKO HAMAMOTO ◽  
SHINGO TOMITA ◽  
KOUKICHI MUNECHIKA
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Back Propagation ◽  
Point Of View ◽  
Network System ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Neural Network System ◽  
Feedback Error ◽  
Error Back Propagation Algorithm

Distortion invariant pattern recognition is an interesting problem from the biological and technological point of view. However, it has not yet been solved by neural networks in satisfactory way. This paper investigates an associative neural network system to improve the recalling accuracy for distortion patterns. On a perception type of neural network with feedback, error back-propagation algorithm and energy function are used for a learning process and a recalling process, respectively. By using gradated patterns as learning and unknown patterns, it is shown that the recalling accuracy becomes higher than using original pattern themselves.

Sign in / Sign up

Export Citation Format

Share Document