scholarly journals APPLICATION OF COMPUTATIONAL TECHNOLOGIES FOR SOLUTION OF PROBLEM OF STOCK MARKET

2020 ◽  
Vol 70 (2) ◽  
pp. 14-20
Author(s):  
D.B. Amirkhan ◽  
◽  
A.B. Shansharkhanov ◽  
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Single Layer ◽  
Prediction Algorithm ◽  
Program Code ◽  
Delta Rule ◽  
Network Training ◽  
Numerical Testing ◽  
The Exchange Rate

The article discusses a method for forecasting the exchange rate. Artificial neural networks act as a forecasting tool. As a currency for the numerical testing of the proposed approach, the oil price in dollars, USD (value in rubles and tenge) was chosen as the most common currency in the world. The data will be processed from 2000 to 2019. In the course of the study, the indicators of the General exchange rate were identified with each other by day. When determining the dollar exchange rate using a single-layer neural network, the Adeline algorithm and the generalized Delta rule were used. Based on the prediction algorithm, the program code is written in Python. It is obvious that the quality of neural network training can be used to further predict the dynamics of the exchange rate.

The Application Process of Wavelet Neural Network in Intelligent Evaluation of the Quality of Engineering Anchor Poles

2011 ◽  
Vol 243-249 ◽  
pp. 2969-2972
Author(s):  
Rui Jun Li ◽  
Ya Qing Shi ◽  
Jian Suo Ma ◽  
Xi Yan Jiang
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Application Process ◽  
Destructive Testing ◽  
Network Training ◽  
Quality Detection ◽  
Non Destructive ◽  
The Ideal

Most detection means on the anchorage integrity today still remain on the destructive testing level, which can hardly meet the actual needs of quality detection on large volumes of anchor poles in the anchorage engineering. This paper presents the application process of wavelet neural network in the non-destructive intelligent testing on the quality of engineering anchor poles. Taking the project of "Management Buildings and Museum of China Marine Sports School" in Qingdao as an example, this paper uses neural toolbox of MATLAB to do the network training by selecting training and simulation samples. The ideal training results indicate that with the help of neural toolbox of MATLAB, the application process of wavelet neural network can not only make intelligent evaluation of the quality of engineering anchor poles, but also make up traditional means, which can not detect large volumes of anchor poles.

Compact Disc Visual Inspection Using Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 727-732
Author(s):  
Anton Satria Prabuwono ◽  
Siti Rahayu Zulkipli ◽  
Doli Anggia Harahap ◽  
Wendi Usino ◽  
A. Hasniaty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Visual Inspection ◽  
Processing Technique ◽  
Compact Disc ◽  
Image Processing Technique ◽  
Accuracy Rate ◽  
Network Training ◽  
Fields Of Study

Image processing is widely used in various fields of study including manufacturing as product inspection. In compact disc manufacturing, image processing has been implemented to recognize defect products. In this research, we implemented image processing technique as pre-processing processes. The aim is to acquire simple image to be processed and analyzed. In order to express the object from the image, the features were extracted using Invariant Moment (IM). Afterward, neural network was used to train the input from IM’s results. Thus, decision can be made whether the compact disc is accepted or rejected based on the training. Two experiments have been done in this research to evaluate 40 datasets of good and defective images of compact discs. The result shows that accuracy rate increased and can identify the quality of compact discs based on neural network training.

scholarly journals Investigation of the Batch Size Influence on the Quality of Text Generation by the SeqGAN Neural Network

2021 ◽  
Author(s):  
Nikolay Krivosheev ◽  
Ksenia Vik ◽  
Yulia Ivanova ◽  
Vladimir Spitsyn
Keyword(s):  
Neural Network ◽  
Monte Carlo Algorithm ◽  
Batch Size ◽  
Preliminary Training ◽  
Text Generation ◽  
Further Training ◽  
Adversarial Learning ◽  
Network Training ◽  
Testing Algorithms

One of the problems of text generation using the LSTM neural network is a decrease in the quality of generation with an increase in the length of the generated text. There are various solutions to improve the quality of text generation based on generative adversarial neural networks. This work uses preliminary training of the LSTM neural network based on the MLE approach and further training based on the SeqGAN neural network. Based on the presented results, we can conclude that the SeqGAN-based approach allows to increase the quality of text generation according to the NLL and BLEU metrics. The study of the influence of the batch size, in the process of competitive training of the SeqGAN neural network, on the quality of text generation has been carried out. It is shown that with an increase in the batch size, in the process of adversarial learning, the quality of LSTM neural network training increases. In this work, the Monte Carlo algorithm is not used in the training process of the SeqGAN neural network. For training and testing algorithms, image captions from the COCO Image Captions data sample are used. The quality of text generation based on the NLL and BLEU metrics has been assessed. Examples of the results of generating texts with an assessment of the quality of examples according to the BLEU metric are given,

Noise Injection: Theoretical Prospects

1997 ◽  
Vol 9 (5) ◽  
pp. 1093-1108 ◽  
Author(s):  
Yves Grandvalet ◽  
Stéphane Canu ◽  
Stéphane Boucheron
Keyword(s):  
Neural Network ◽  
Cost Function ◽  
Heat Kernel ◽  
Smoothness Assumption ◽  
Network Training ◽  
Gaussian Analysis ◽  
Noise Injection ◽  
Global Smoothness ◽  
Input Perturbation

Noise injection consists of adding noise to the inputs during neural network training. Experimental results suggest that it might improve the generalization ability of the resulting neural network. A justification of this improvement remains elusive: describing analytically the average perturbed cost function is difficult, and controlling the fluctuations of the random perturbed cost function is hard. Hence, recent papers suggest replacing the random perturbed cost by a (deterministic) Taylor approximation of the average perturbed cost function. This article takes a different stance: when the injected noise is gaussian, noise injection is naturally connected to the action of the heat kernel. This provides indications on the relevance domain of traditional Taylor expansions and shows the dependence of the quality of Taylor approximations on global smoothness properties of neural networks under consideration. The connection between noise injection and heat kernel also enables controlling the fluctuations of the random perturbed cost function. Under the global smoothness assumption, tools from gaussian analysis provide bounds on the tail behavior of the perturbed cost. This finally suggests that mixing input perturbation with smoothness-based penalization might be profitable.

High-resolution soil moisture retrieval using a Neural Network approach from Sentinel-1 SAR data

2020 ◽  
Author(s):  
Qi Gao ◽  
Maria Jose Escorihuela ◽  
Nemesio Rodriguez-Fernandez ◽  
Olivier Merlin ◽  
Mehrez Zribi
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
High Resolution ◽  
Neural Network Training ◽  
Neural Network Approach ◽  
Auxiliary Data ◽  
Network Training ◽  
The Neural Network ◽  
Sar Data

<p>High-resolution soil moisture product is important for agriculture-related managements including irrigation. We have investigated the Change Detection (CD) method using Sentinel-1 data for 100 m resolution soil moisture retrieval and got a Root Mean Square Error (RMSE) about 0.6 m<sup>3</sup>/m<sup>3</sup>. However, the result of this approach is not accurate enough for high-density crops like corn. Another approach needs to be studied to get better accuracy over all types of crops. The artificial neural network (NN) technique, which involves nonlinear parameterized mapping from an input vector to an output vector, is an appropriate tool for retrieving geophysical parameters from remote sensing data. Many studies have explored the NN approach for processing remotely sensed data, including retrieving soil moisture, however, only a few studies [Notarnicola et al., 2010; Paloscia et al., 2013, etc.] had investigated NN for soil moisture estimation over vegetation-covered areas, especially in a large scale.</p><p>The objective of this study is to develop an approach based on neural networks to estimate soil moisture at high resolution over vegetation-covered areas from Sentinel-1 C-band SAR data. The quality of the output results depends directly on the quality of the input data used to train the NN and the reference data for the training, therefore, we performed our study over Catalonia, where we have many auxiliary data. The study is performed using both VV and VH polarization over the whole Catalonia. Apart from Sentinel-1 SAR data, auxiliary data including Sentinel-2 NDVI, SMAP soil moisture, CCI (ESA Climate Change Initiative) land cover, SIGPAC (Sistema de Información Geográfica de Parcelas Agrícolas) land cover, irrigation index and crop type information from SIGPAC, and DEM (Digital elevation model) are also used for approach development. DISPATCH (Disaggregation based on Physical and Theoretical scale Change) soil moisture product at 1 km resolution is considered as the target in the Neural Network training, adding great value to our study. To prepare the Neural Network training, all data sets are co-registered at 1 km resolution within the same size and resampled for the same dates within one year (2017). Two indexes describing the normalized backscatter difference and soil moisture are introduced as equation (1) and (2):</p><table><tbody><tr><td>Index<sub>1 </sub>= (σ<sup>0</sup><sub>i </sub>- σ<sup>0</sup><sub>min</sub>) / (σ<sup>0</sup><sub>max </sub>- σ<sup>0</sup><sub>min</sub>)</td> <td>(1)</td> </tr><tr><td>Index<sub>2 </sub>= SM<sub>min</sub> + (SM<sub>max </sub>- SM<sub>min</sub>) * Index<sub>1</sub></td> <td>(2)</td> </tr></tbody></table><p> </p><p>Different parameters were tested to train the Neural Network approach, the preliminary results show a correlation value compared with DISPATCH product about 0.71 over croplands, 0.73 over irrigated fields, and 0.65 over forests, considering Index1, Index2 and SMAP soil moisture. Works are still on-going to try to improve the results by better analyzing the SAR data performance over different fields and conditions. The final goal of the study is to produce 100 m resolution soil moisture product. After 1 km resolution study, we will apply the approach at 100m resolution, and the in-situ soil moisture will be used for validation.</p><p>This work is inscribed within the Water4Ever project, which is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme. </p>

scholarly journals EFFICIENCY ESTIMATION OF PARALLEL ALGORITHM OF ENHANCED HISTORICAL DATA INTEGRATION ON COMPUTATIONAL GRID

2014 ◽  
pp. 9-19
Author(s):  
V. Turchenko ◽  
C. Triki ◽  
Lucio Grandinetti ◽  
Anatoly Sachenko
Keyword(s):  
Neural Network ◽  
Parallel Algorithm ◽  
Historical Data ◽  
Single Layer ◽  
Computing System ◽  
Neural Network Training ◽  
Training Time ◽  
Network Training ◽  
Dynamic Mapping ◽  
Mapping Criterion

The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.

scholarly journals Nilai Tukar Petani dan Pola Konsumsi Rumah Tangga (Studi Kasus: Petani Tanaman Pangan dan Hortikultura di Kecamatan Sabak Timur Kabupaten Tanjung Jabung Timur)

2020 ◽  
Vol 5 (1) ◽  
pp. 77
Author(s):  
Evi Adriani ◽  
Hasminidiarty Hasminidiarty ◽  
Ali Fahmi
Keyword(s):  
Exchange Rate ◽  
Food Consumption ◽  
Consumption Patterns ◽  
Primary Data ◽  
Consumption Pattern ◽  
Purchasing Power ◽  
Dragon Fruit ◽  
Significant Difference ◽  
The Exchange Rate

The structure of household expenditure describe the purchasing power of households. For farmer households, their purchasing power is reflected in the exchange rate of the farmers (NTP). NTP describe the higher purchasing power is also higher, which will influence the consumption patterns of farmers.This study aims to: 1) describe the characteristics of farmers, 2) calculate the exchange rate farmers (NTP) and 3) to analyze the pattern of household consumption and horticulture crop farmers in the Eastern District of Muara Sabak. Primary data type with purposive sampling method, were analyzed with descriptive methods in the form of table distibusi frequency and cross tabulation. The results showed: 1) 82.5%farmers belonging to the productive age, the program received by farmers only PAJALELE program, the number of household members mostly 3-4 people and no one including smallholders to own property ownership status of more than 50%. Crops planted by farmers are rice, corn and beans (soy and peanut) and horticultural chili, cucumber, beans, cabbage, eggplant, kale, dragon fruit and litchi. A total of 95% had a second job, the main income of the average farmer Rp. 14,453,162 per growing season or Rp. 3,613,290.5 per month, the average revenue side Rp.2.658.667; 2)NTP did not show a significant difference to household NTP (NTPRP) respectively are 1.47045 and 1.67339 with NTP value distributions for each respondent so lame; 3) The consumption pattern comprising farmers of food and non food consumption in which the average number of non-food consumption is greater than consumption of food. Food consumption is dominated by rice, while non contribute most to the consumption of processed foods. Consumption expenditure for transportation of proportion is highest among the other non-food consumption. Advisable to socialize and educate farmers about the smart and healthy consumption patterns that consumption of great benefit to the quality of human resources and increase agricultural production.

scholarly journals Penerapan Algoritma Optimasi Chaos pada Jaringan Ridge Polynomial untuk Prediksi Jumlah Pengangguran

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Rina Pramitasari ◽  
Retantyo Wardoyo
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Single Layer ◽  
Higher Order ◽  
West Kalimantan ◽  
Chaos Optimization ◽  
Network Training ◽  
Polynomial Neural Network ◽  
Chaos Optimization Algorithm ◽  
Neuron Layer

AbstrakRidge polynomial neural network (RPNN) awalnya diusulkan oleh Shin dan Ghosh, dibangun dari jumlah peningkatan order pi-sigma neuron (PSN). RPNN mempertahankan pembelajaran cepat, pemetaan yang kuat dari layer tunggal higher order neural network (HONN) dan menghindari banyaknya bobot karena meningkatnya sejumlah input. Algoritma optimasi chaos digunakan dengan memanfaatkan persamaan logistik yang sensitif terhadap kondisi awal, sehingga pergerakan chaos dapat berubah di setiap keadaan dalam skala tertentu menurut keteraturan, ergodik dan mempertahankan keragaman solusi.Algoritma Optimasi Chaos diterapkan pada RPNN dan digunakan untuk prediksi jumlah pengangguran di Kalimantan Barat. Proses pelatihan jaringan menggunakan ridge polynomial neural network, sedangkan pencarian nilai awal bobot dan bias jaringan menggunakan algoritma optimasi chaos. Struktur yang digunakan terdiri dari 6 neuron layer input dan 1 neuron layer output. Data diperoleh dari Badan Pusat Statistik.Hasil dari penelitian ini menunjukkan bahwa algoritma yang diusulkan dapat digunakan untuk prediksi. Kata kunci—prediksi jumlah pengangguran, jaringan syaraf tiruan, algoritma optimasi chaos, ridge polynomial neural network  Abstract Ridge polynomial neural network was initially proposed by Shin and Ghosh, made of total increased pi-sigma neural (PSN) orders. Ridge polynomial neural network maintains quick learning, strong mapping of single layer of higher order neural network (HONN) and avoids many weights because total increased inputs. Chaos optimization algorithm is used by utilizing sensitive logistic equation to initial condition, so that chaos movement can change in each condition in specific scale according to orderliness, ergodic, and maintaining solution variety.             Chaos optimization algorithm is applied to ridge polynomial neural network and used to predict total unemployed persons in West Kalimantan. Network training process used ridge polynomial neural network; while, initial values and weights and bias of network were found using Chaos optimization algorithm. Structure used consisted of 6 input layer neurons and one output layer neuron. Data were obtained from Central Statistic Agency.            The results of research indicated that algorithm proposed could be used to predict Keywords— predict the number of unemployed, neural networks, chaos optimization algorithm, ridge polynomial neural network

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