Implementation of a vector quantization codebook design technique based on a competitive learning artificial neural network

1992 ◽  
Vol 5 (4) ◽  
pp. 307-330
Author(s):  
Stanley C. Ahalt ◽  
Prakoon Chen ◽  
Cheng-Taou Chou ◽  
Tzyy-Ping Jung
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Competitive Learning ◽  
Design Technique ◽  
Codebook Design ◽  
Artificial Neural

scholarly journals Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

2020 ◽  
Vol 4 (2) ◽  
pp. 75-85
Author(s):  
Chrisani Waas ◽  
D. L. Rahakbauw ◽  
Yopi Andry Lesnussa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Processing System ◽  
Disease Diagnosis ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Neural Network Learning ◽  
Artificial Neural

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.

scholarly journals Analysis of the experimental positron lifetime spectra by neural networks

2003 ◽  
Vol 18 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Senada Avdic ◽  
Roumiana Chakarova ◽  
Imre Pazsit
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Positron Lifetime ◽  
Learning Vector Quantization ◽  
Polymer Materials ◽  
Analysis Tool ◽  
Spectral Components ◽  
Artificial Neural

This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pzzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.

scholarly journals Weather Forecast using Learning Vector Quantization Methods

2021 ◽  
Vol 1 ◽  
pp. 69-74
Author(s):  
Siska Andriani ◽  
Kotim Subandi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Weather Forecasting ◽  
Daily Life ◽  
Weather Forecast ◽  
Learning Vector Quantization ◽  
Weather Forecasts ◽  
Network Methods ◽  
Artificial Neural

Weather forecasting is one of the important factors in daily life, as it can affect the activities carried out by the community. The study was conducted to optimize weather forecasts using artificial neural network methods. The artificial neural network used is a learning vector quantization (LVQ) method, in which artificial neural networks based on previous research are suitable for prediction. The research is modeling weather forecast optimization using the LVQ method. Models with the best accuracy can be used in terms of weather forecasts. Based on the results of the training that has been done in this study produces the best accuracy on the LVQ method which is to produce 72%.

scholarly journals Prediksi Jenis Cacing Nematoda Usus Yang Menginfeksi Siswa Dengan Menggunakan Metoda LVQ

2017 ◽  
Vol 8 (2) ◽  
pp. 170-184
Author(s):  
Erni Rouza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Neural Network Learning ◽  
Intestinal Nematode ◽  
Artificial Neural ◽  
Level Of Training ◽  
Artificial Neural Network Ann

Abstrak-Pada saat ini, Jaringan Syaraf Tiruan (JST) telah banyak menjadi objek penelitian yang menarik, karena penerapannya sangat potensial dalam berbagai bidang sains, salah satu penerapannya didalam memprediksi penyakit. Penelitian ini bertujuan untuk mencoba menerapkan metode Learning vector Quantization (LVQ) dalam memprediksi jenis cacing Nematoda usus yang menginfeksi siswa dari nilai akurasi yang dihasilkan, karena beberapa penelitian menunjukkan bahwa anak usia sekolah dasar merupakan golongan yang sering terkena infeksi cacing usus. Dari hasil pelatihan dan pengujian menggunakan metode Learning Vector Quantization (LVQ) diketahui bahwa tingkat akurasi sesuai dengan hasil sebenarnya dan nilainya konstan, proses cepat hanya membutuhkan waktu paling lama 3 menit dan memberikan hasil yang optimal yaitu tingkat akurasi data latih sebesar 78,6885%, serta 80% untuk data uji. Hal ini menunjukkan bahwa jaringan yang terbentuk sudah cukup baik, akurat dan cepat dalam melakukan pembelajaran terhadap data input yang diberikan dalam memprediksi jenis cacing Nematoda Usus yang menginfeksi siswa. Kata kunci : Cacing Nematoda Usus, Jaringan Syaraf Tiruan, Learning Vector Quantization Abstract- At this time, an Artificial Neural Network (ANN) has been an interesting objects of research, because of application has potential in various fields of science, one application was used to predict diseases. This study aims to try to implement methods Learning vector quantization (LVQ) in predicting the type of Nematode worms that infect the intestines of students from the resulting accuracy value, because some studies show that children of primary school age are often exposed to a class of intestinal worm infections. From the results of the training and testing using methods Learning Vector Quantization (LVQ) note that the level of accuracy in accordance with the actual results and the value of the constant, quick process only takes a maximum of 3 minutes and provide optimal results is the level of training data accuracy of 78.6885%, and 80% for the test data. This indicates that the network is formed is quite good, accurate and fast in doing the learning on the input data given in predicting Intestinal Nematode worm species that infect students. Keywords: Intestinal Netamoda Worms, Artificial Neural Network, Learning Vector Quantization

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