IDENTIFIKASI POLA IRIS MENGGUNAKAN METODE BACKPROPAGATION

2019 ◽  
Vol 8 (02) ◽  
pp. 43-48
Author(s):  
Yoan Elviralita ◽  
Asrul Hidayat
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Unknown Parameter ◽  
Recognition Rate ◽  
Backpropagation Neural Network ◽  
Excellent Performance ◽  
Controlling System

In recent years, there has been a lot of research related to pattern recognition is conducted to identify various forms of patterns and controlling system. Utilizing backpropagation neural network in pattern identifying is very useful to solve problems with unknown parameter and difficult to determined. And then the data of the pattern are trained and tested. The results obtained from the recognition rate indicates a backpropagation neural network, provide excellent performance, which is an average of 98%. This neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

EXPLORING FEATURES AND CLASSIFIERS TO CLASSIFY GENE EXPRESSION PROFILES OF ACUTE LEUKEMIA

2002 ◽  
Vol 16 (07) ◽  
pp. 831-844 ◽  
Author(s):  
SUNG-BAE CHO
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Recognition Rate ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Self Organizing Map ◽  
Backpropagation Neural Network ◽  
Self Organizing

Bioinformatics has recently drawn a lot of attention to efficiently analyze biological genomic information with information technology, especially pattern recognition. In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers. The gene information from a patient's marrow expressed by DNA microarray, which is either the acute myeloid leukemia or acute lymphoblastic leukemia, is used to predict the cancer class. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification. Experimental results indicate that backpropagation neural network with Pearson's correlation coefficients produces the best result, 97.1% of recognition rate on the test data.

scholarly journals Batik pattern recognition using convolutional neural network

2020 ◽  
Vol 9 (4) ◽  
pp. 1430-1437
Author(s):  
Mohammad Arif Rasyidi ◽  
Taufiqotul Bariyah
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Network Architecture ◽  
Experimental Results ◽  
Training Data ◽  
Excellent Performance ◽  
Artistic Value

Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.

Application of ART-2 Neural Network and Invariant Moment in Image Pattern Recognition

2012 ◽  
Vol 433-440 ◽  
pp. 4014-4019 ◽  
Author(s):  
Lei Hao ◽  
Yue Hua Gao ◽  
Rui Jun Jia
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Recognition Rate ◽  
Experimental Results ◽  
Invariant Moment ◽  
Recognition Model ◽  
Image Pattern

This paper mainly uses image pre-processing and feature extraction to calculate the invariant moment of image, and ultimately realizes the image pattern recognition based on ART-2 neural network. Experimental results show that ART-2 neural network has high recognition rate. It also solves the contradiction between network's plasticity and stability, when new recognition model appears.

scholarly journals Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinfeng Ge ◽  
Jing Zhang ◽  
Ye Zhou ◽  
Jianguo Cai ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hybrid Model ◽  
Rough Set ◽  
Recognition Rate ◽  
Zernike Moment ◽  
Calculation Error ◽  
Time Step

In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits.

scholarly journals Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3170 ◽  
Author(s):  
Zhang ◽  
Yang ◽  
Qian ◽  
Zhang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Recognition System ◽  
Test Method ◽  
Training Dataset ◽  
Artificial Neural ◽  
Semg Signals

In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.

scholarly journals Recognition of Brahmi Words by Using Deep Convolutional Neural Network

2020 ◽  
Author(s):  
Neha Gautam ◽  
Soo See Chai ◽  
Jais Jose
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Image Database ◽  
Deep Convolutional Neural Network ◽  
Significant Progress ◽  
Practical Operation ◽  
Series Of Experiments ◽  
Made In

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

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