5517598 Error back-propagation method and neural network system

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.

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

1993 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.1993.394137 ◽

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

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NEUROSLAB – neural network system for horizontal formwork selection

Canadian Journal of Civil Engineering ◽

10.1139/l95-088 ◽

1995 ◽

Vol 22 (4) ◽

pp. 785-792 ◽

Cited By ~ 3

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.

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

Defect and Diffusion Forum ◽

10.4028/www.scientific.net/ddf.365.194 ◽

2015 ◽

Vol 365 ◽

pp. 194-199 ◽

Cited By ~ 6

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.

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

International Journal of Image Processing and Vision Science ◽

10.47893/ijipvs.2012.1010 ◽

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%.

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