backpropagation method
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MAUSAM ◽  
2022 ◽  
Vol 53 (4) ◽  
pp. 471-480
Author(s):  
S. PAL ◽  
J. DAS ◽  
P. SENGUPTA ◽  
S. K. BANERJEE

In this paper, a neural network based forecasting model for the maximum and the minimum temperature for the ground level is proposed. A backpropagation method of gradient-decent learning in multi-layer perceptron (MLP) type of neural network with only one hidden layer is considered. This network consists of 25 input nodes and two output nodes. The network is trained with a varying number of nodes in the hidden layer using a set of training sample and each of them is tested with a set of test sample. It accepts previous two consecutive days information (such as pressures, temperatures, relative humidities, etc.) as inputs for the estimation of the maximum and the minimum temperature as output. The network with 20 or less neurons in the hidden layer is found to be "optimum" and it produces an error within ±2° C for 80% of test cases.


Author(s):  
Annisa Mujahidah Robbani ◽  
I Gede Pasek Suta Wijaya ◽  
Fitri Bimantoro

Abstract-The literature shows that Graphology is common and relatively useful in our life. For example, as one of the job requirements. Professional organizations hire a professional handwriting analyst called Graphologist to analyze the characteristic traits of the candidates by identified their handwriting. However, the accuracy of handwriting analysis depends on how skilled the graphologist is, two graphologists which predict the same handwriting may give us a different result of the prediction. To improve the accuracy, we develop a system that can automatically predict a person’s personality based on the shape of the handwriting of the letters "i", "o", and "t" using the Levenberg Marquardt Backpropagation method. Based on this research we got the maximum accuracy by using 2 hidden layers. We got 71,42% of accuracy for the letter “i”, 76,92% of accuracy for the letter “o”, and 60% of accuracy for letter the “t”.


2021 ◽  
pp. 181-184
Author(s):  
Jhon Veri ◽  
Surmayanti Surmayanti ◽  
Guslendra Guslendra

We analyzed the performance of the artificial neural network with the backpropagation method in predicting crude oil prices in this paper, including the case of crude oil price predictions. The training results obtained that the MSE value was 0.00099762 with 135 Epoch, in the network testing the MSE value was 0.093336. Meanwhile, the predicted value is determined by the target value with a contribution of 99% with a significant effect. Thus the accuracy level is determined by the target value and the predicted value. The accuracy of the system is obtained for 83,6%.


Author(s):  
Данила Владимирович Мамаев ◽  
Сергей Алексеевич Меркурьев ◽  
Ольга Витальевна Малышкина

Авторами получены образцы пьезоэлектрической керамики ниобата калия натрия с концентрацией пор 10,25 и 40 объемных %. Разработана капсульная свёрточная искусственная нейронная сеть для определения процентного содержания пор по изображению. С помощью растрового электронного микроскопа получено обучающее множество примеров (фотографии поверхности и сколов подготовленных образцов). Разработка и апробация капсульной свёрточной искусственной нейронной сети осуществлена в несколько этапов. На первом проведено ее обучение с помощью метода обратного распространения ошибки. На втором - тестирование на проверочном множестве. На заключительном этапе проведено сравнение полученных результатов с результатами метода сравнения плотности материала. Показано, что данный метод можно использовать для решения задачи определения процентного содержания пор в KNN, поскольку полученные результаты сопоставимы с результатами, полученными другим методом. Установлено, что в образцах, в которых не были специально добавлены поры, также присутствуют поры (порядка 5 %). The authors synthesized samples of piezoelectric potassium sodium niobate ceramics of 10,25 and 40 pore percentage by volume. Capsule convolutional artificial neural network has been developed for estimation of the pore percentage in images. Using the scanning electron microscopy, f learning massive of examples was formed (photographs of surface and edges of so-synthesized samples). Development and approbation of the capsule convolutional artificial neural network was completed in a few stages. During the first stage, the network was trained using a backpropagation method. Secondly, it was tested by a testing set. At the final stage we made a comparison of the acquired results with the results of the density comparing method. The article shows that this method can be used the pore percentage estimation in sodium niobate ceramics because the acquired results are comparable with the results of other methods. It was found that the samples where the pores were not made also have some pore percentage (about 5 %).


JURTEKSI ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 85-94
Author(s):  
Muhammad Jufri

Abstract: The population growth in Indonesia is increasing rapidly every year, so to help the government control the population growth through family planning programs, especially in the city of Batam. This study explains and describes one of the Artificial Terms Network methods, namely Backpropagation, where this method can predict what will happen in the future using data and information in the past. This study aims to predict the birth rate in the city of Batam to help the government with the family planning program. The data used is the annual data on the number of births in the city of Batam in 2016-2020 at The Civil Registry Office. To facilitate the analysis of research data, the data were tested using Matlab R2015b. In this study, the training process was carried out using 3 network architectures, namely 4-10-1, 5-18-1, and 4-43-1. Of these 3 architectures, the best is the 4-43-1 architecture with an accuracy rate of 91% and an MSE value of 0.0012205. The Backpropagation method can predict the amount of population growth in the city of Batam based on existing data in the past.           Keywords: artificial neural network; backpropagation; prediction   Abstrak: Pertumbuhan jumlah penduduk diindonesia yang setiap tahun meningkat dengan pesat, maka untuk membantu pemerintah mengendalikan jumlah pertumbuhan penduduk melalui program keluarga berencana khususnya dikota Batam. Penelitian ini  menjelaskan dan memaparkan tentang salah satu metode Jaringan Syarat Tiruan yaitu Backpropagation, dimana metode ini dapat memprediksi apa yang akan terjadi masa yang akan datang dengan menggunakan data dan informasi dimasa lalu. Penelitian ini bertujuan untuk memprediksi tingkat kelahiran di kota Batam sehingga membatu pemerintah untuk perencanaan keluarga berencana. Data yang digunakan yaitu data tahunan jumlah kelahiran di kota Batam pada tahun 2016-2020 pada Dinas Kependudukan dan Catatan Sipil. Untuk mempermudah analisis data penelitian maka, data diuji menggunakan Matlab R2015b. Pada penelitian ini dilakukan proses pelatihan menggunakan  3 arsitektur jaringan yaitu 4-10-1, 5-18-1, dan 4-43-1. Dari ke-3 arsitektur ini yang terbaik adalah arsitektur 4-43-1 dengan tingkat akurasi sebesar 91% dan nilai MSE 0,0012205. Metode backpropagation mampu memprediksi jumlah pertumbuhan penduduk di kota Batam berdasarkan data yang ada dimasa lalu. Kata kunci: backpropagation; jaringan syaraf tiruan; prediksi 


2021 ◽  
Vol 2 (2) ◽  
pp. 130-137
Author(s):  
Slamet Riyadi ◽  
Zilvanhisna Emka Fitri ◽  
Arizal Mujibtamala Nanda Imron

Early childhood has difficulty remembering Latin letters or Roman characters than adults. Some of the factors that cause it are cognitive development, motivation, interest in learning, emotions and environmental factors. To overcome this, an innovative media is needed so that children can easily remember Latin letters. One of the innovative media applies digital image processing techniques and artificial intelligence. The fonts used are 10 types of letter models with image processing techniques such as preprocessing, binaryization, pixel mapping and creating vector as feature extraction.  While the artificial intelligence used is the backpropagation method. The total data is 208 letter images with 625 input features with 500 epochs, the best learning rate used by the system is 0.025 so that the best training accuracy is 93.96% and testing accuracy is 92.31%.


2021 ◽  
Vol 2092 (1) ◽  
pp. 012013
Author(s):  
Krivorotko Olga ◽  
Liu Shuang

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.


Author(s):  
Lady Silk Moonlight ◽  
Fiqqih Faizah ◽  
Yuyun Suprapto ◽  
Nyaris Pambudiyatno

Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning 


2021 ◽  
pp. 1-18
Author(s):  
M.L. Sworna Kokila ◽  
Dr. V. Gomathi

Automatic Person Re-identification by video surveillance is commonly used in different applications. Perhaps the human uniqueness criteria for tracking the presence of the same person across multiple camera views and a person’s growth identification is extremely challenging. To solve the above problem, we propose an efficient Auto Track Regression System (ATRF) based on a deep learning technique that uses an eminent representation strategy along with recognition. In this work, the Auto Wiley Detective (AWD) approach is proposed for the representation of features that can collect valuable information by monitoring individuals. After obtaining important information on the characteristics, it is possible to define the personal growth identity of the generation. The OPVC (Original Pick Virtual Classifier) is used for accurate classification of the queried person from a dense area by utilizing features of a person’s growth identity extracted from feature extraction by the Auto Wiley Detection Method. The proposed Originated Pick Virtual Classifier (OPVC) uses Platt scaling (originated pick) on probit regression (virtual) to train the featured data set for accurate person re-identification, which is boosted by the Karush–Kuhn–Tucker (KKT) conditions to reduce false re-identification. Since the gallery information is trained using the Backpropagation method and smoothened analysis through approximated output, the Auto Wiley Detection Method proficiently detects the required information automatically. This also helps to detect the person query image from the database, which contains a vast collection of video images based on the similarity features identified in the query image and the detailed features extracted from the query image. The classification is completed automatically, and then the Person Re-Identification from the databases is performed accurately and efficiently. Henceforth, the proposed work effectively extracts reliable height and age estimates with improved flexibility and individual re-identifying capabilities.


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