Minimizing the negative effect of the overlapping pixels on the classification accuracy of the error back-propagation neural network classifier using the ancillary and supplemental data

2004 ◽  
Vol 30 (2) ◽  
pp. 157-168 ◽  
Author(s):  
Fathi S Ikweiri ◽  
Yee-Chung Jin ◽  
Bradley A Wilson
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Classifier ◽  
Error Back Propagation ◽  
Negative Effect

scholarly journals Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network 

2014 ◽  
Vol 32 (No. 3) ◽  
pp. 280-287 ◽  
Author(s):  
I. Golpour ◽  
ParianJA ◽  
R.A. Chayjan
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Rice Cultivars ◽  
Image Processing Algorithm ◽  
White Rice ◽  
Colour Features

We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

scholarly journals Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kuo-Nan Yu ◽  
Her-Terng Yau ◽  
Jian-Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Solar Photovoltaic ◽  
Error Back Propagation ◽  
Point Tracking ◽  
Diagnosis Method

At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

Application of Neural Network in Free Air Ball Forming for Wire Bonder

1999 ◽  
Author(s):  
Jau-Liang Chen ◽  
Yeh-Chao Lin ◽  
Chun-Hsien Liu ◽  
Wen-Chang Kuo ◽  
Tzung-Ching Lee
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Tail Length ◽  
Back Propagation Neural Network ◽  
Ball Bond ◽  
Error Back Propagation ◽  
Fine Pitch ◽  
Free Air ◽  
Network Method

Abstract The shape and size of free-air-ball formation deeply affect the quality of wire bonding. It not only affects the bondability of first bond (ball bond), but also affects the possibility of processing low loop height bonding for thin form packages and high I/O fine pitch packages. Several parameters, such as tail length, spark gap, supplied voltage, current and time of electrical flame-off unit etc., will affect the free-air-ball formation. This paper represents a study of using error-back-propagation neural network method to analyze the effect of each parameter and to predict the final result of the ball forming. From the experiment, it is shown that neural network can not only be used to precisely predict the size of ball formation, but also saves sampling time.

Dynamic Load Balancing Using Hybrid Approach

2019 ◽  
Vol 9 (3) ◽  
pp. 75-88
Author(s):  
Sunita Gond ◽  
Shailendra Singh
Keyword(s):  
Neural Network ◽  
Load Balancing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Error Back Propagation ◽  
Proposed Model ◽  
Multiple Process ◽  
Trained Neural Network ◽  
Sequence Quality

Load balancing in a cloud environment for handling multiple process of different size is an important issue. Many advanced technologies are incorporated in the processes-based resource allocation which enhances the system efficiency. The steps of allotting resources to process can be done by taking data which helps to analyze and make important decisions at runtime. This article focuses on the allocation of cloud resources where two models were developed, the first was TLBO (Teacher Learning Based Optimization), a genetic algorithm which finds the correct position for the process to execute. Here, some information used for analysis was total number of machines, memory, execution time, etc. So, the output of the TLBO process sequence was used as training input for the Error Back Propagation Neural Network for learning. This trained neural network improved the work job sequence quality. Training was done in such a way that all sets of features were utilized to pair with their process requirement and current position. For increasing the reliability of the work, an experiment was done on a real dataset. Results show that the proposed model has overcome various evaluation parameters on a different scale as compared to previous approaches adopted by researchers.

scholarly journals Using Artificial Intelligence System for Pattern Identification Based on Features Extracted from Gel Electrophoresis Images

2020 ◽  
Vol 11 (2) ◽  
pp. 1944-1952
Author(s):  
Sarmad M. Hadi ◽  
Al-Faiz M Z ◽  
Ali A. Ibrahim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Gel Electrophoresis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Artificial Intelligence System ◽  
Average Standard Deviation ◽  
Learning Time ◽  
Intelligence System ◽  
Error Back Propagation

Artificial intelligence has many branches of image processing-based applications in terms of classification and identification, error back-propagation neural network is a great match for such applications as long as linear vector quantization (LVQ) and pattern recognition is another great match for recognition of digital images based on their features. The dataset used in this paper are gel electrophoresis images where 6 features had been extracted from the images and used as input to a neural network for learning and then checked for recognition purposed and the system managed to recognize all the 6 images. Six features had been used: average, standard deviation, smoothness, skewness, uniformity, and entropy. A tiny error rate where allowed in the recognition program to cover the variation of the dataset and the test data (gel-electrophoresis images). The proposed system had successfully managed to identify all of the learned data in both LVQ and error-back-propagation. Error-back-propagation proved itself as a great tool in terms of learning time compared with LVQ, which was very slow in terms of learning time and recognition.

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