scholarly journals Implementasi Multilayer Perceptron Pada Jaringan Saraf Tiruan Untuk Memprediksi Nilai Valuta Asing

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Tommy Ferdian Hadimarta ◽  
Rani Rotul Muhima ◽  
Muchamad Kurniawan
Keyword(s):  
Time Series ◽  
Foreign Exchange ◽  
Multilayer Perceptron ◽  
Supply And Demand ◽  
Learning Rate ◽  
Process Data ◽  
Nerve Network ◽  
Price Patterns ◽  
Available Resources

Abstract. In the context of FOREX investment, the fluctuation of currency becomes a common thing in which movement is greatly influenced by supply and demand. If the demand is higher, the price will increase and conversely, if the supply is higher, the price will go downward. There is a principle that the behavior of price patterns will repeat randomly and make unpredictable movement of FOREX. These patterns of currency fluctuation have deceived many investors and brought losses and even capital failure. Basically, the value of foreign exchange belongs to the data of time series and Multilayer Perceptron is very suitable to process data of time series as it is often used to make prediction. Therefore, this research aimed at implementing Multilayer Perceptron in the artificial nerve network for predicting the value of foreign exchange on the available resources using the attributes of open, high, low, and close. To process the data from the existing attributes, there must be initialization first in X1 (open), X2 (high), and X3 (low) as the inputs and Y (close) as the data target, and then they were normalized so as to calculate sigmoid. The increasing number of epoch does not guarantee that the errors will be smaller. On the contrary, perhaps, the error value will increase. The best result of training occurred by epoch 200 and learning rate 3 within the smallest values of MSE 281.02518, MAD 13.168, and deviation standard 10.294.

scholarly journals Multilayer Perceptron untuk Prediksi Sessions pada Sebuah Website Journal Elektronik

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Aji Prasetya Wibawa ◽  
Widya Lestar ◽  
Agung Bella Putra Utama ◽  
Irzan Tri Saputra ◽  
Zahra Nabila Izdihar
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Learning Rate ◽  
Training Set ◽  
Test Set

Peramalan session website journal dilakukan untuk pendukung pengambilan keputusan dalam rangka meningkatkan kualitas dan nilai akreditasi pada website jurnal. Data sessions dianalisis berdasarkan pergerakan pola data time series menggunakan metode multilayer perceptron. Karakteristik yang dimiliki oleh multilayer perceptron yaitu keunggulan dalam penentuan nilai bobot yang lebih baik daripada metode lain, multilayer perceptron dapat digunakan tanpa pengetahuan sebelumnya dan algoritma dapat diimplementasikan dengan mudah serta mampu menyelesaikan masalah linear dan nonlinear sehingga nilai peramalan menjadi lebih baik. Penelitian menggunakan berbagai persentase data train dan test. Perbandingan data train dan test yang memiliki nilai terbaik adalah 80% data train dan 20% data test dengan learning rate 0.4 dan arsitektur 2-1-1. Hasil evaluasi model diperoleh nilai MSE dan RMSE, 0.015357 dan 0.123999 untuk training set serta, 0.018996 dan 0.137826 untuk MSE dan RMSE dari test set. Waktu eksekusi yang dibutuhkan untuk melakukan peramalan adalah 580.0651 second atau 9.667751 menit.

A Review of Aquaculture Practices and Their Impacts on Chemical Food Safety from a Regulatory Perspective

2015 ◽  
Vol 98 (3) ◽  
pp. 541-549 ◽  
Author(s):  
Joe O Boison ◽  
Sherri B Turnipseed
Keyword(s):  
Food Security ◽  
Food Safety ◽  
Foreign Exchange ◽  
Fish Species ◽  
Food Production ◽  
Supply And Demand ◽  
Fish Products ◽  
Regulatory Perspective ◽  
The World ◽  
Aquaculture Industry

Abstract Aquaculture is currently one of the most rapidly growing food production industries in the world. The increasing global importance for this industry stems primarily from the fact that it is reducing the gap between the supply and demand for fish products. Commercial aquaculture contributes significantly to the economies of many countries since high-value fish species are a major source of foreign exchange. This review looks at the aquaculture industry, the issues raised by the production of fish through aquaculture for food security, the sustainability of the practice to agriculture, what the future holds for the industry in the next 10-20 years, and why there is a need to have available analytical procedures to regulate the safe use of chemicals and veterinary drugs in aquaculture.

Liquid State Machine to Generate the Movement Profiles for the Gait Cycle of a 6 DOF Bipedal Robot in a Sagittal Plane

2018 ◽  
Author(s):  
Jesús Franco-Robles ◽  
Alejandro De Lucio-Rangel ◽  
Karla A. Camarillo-Gómez ◽  
Gerardo I. Pérez-Soto ◽  
Jesús Rivera-Guillén
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Liquid State ◽  
Gait Cycle ◽  
Sagittal Plane ◽  
Experimental Result ◽  
Forward Kinematics ◽  
Neuronal System ◽  
Bipedal Robot ◽  
Input Time

In this paper, a neuronal system with the ability to generate motion profiles and profiles of the ZMP in a 6DoF bipedal robot in the sagittal plane, is presented. The input time series for LSM training are movement profiles of the oscillating foot trajectory obtained by forward kinematics performed by a previously trained ANN multilayer perceptron. The profiles of objective movement for training are acquired from the analysis of the human walk. Based on a previous simulation of the bipedal robot, a profile of the objective ZMP will be generated for the y–axis and another for the z–axis to know its behavior during the training walk. As an experimental result, the LSM generates new motion profiles and ZMP, given a different trajectory with which it was trained. With the LSM it will be possible to propose new trajectories of the oscillating foot, where it will be known if this trajectory will be stable, by the ZMP, and what movement profile for each articulation will be required to reach this trajectory.

scholarly journals Do natural rubber price bubbles occur?

2019 ◽  
Vol 65 (No. 2) ◽  
pp. 67-73 ◽  
Author(s):  
Chi-Wei Su ◽  
Lu Liu ◽  
Ran Tao ◽  
Oana-Ramona Lobonţ
Keyword(s):  
Time Series ◽  
Natural Rubber ◽  
Supply And Demand ◽  
Oil Prices ◽  
Climatic Changes ◽  
Price Bubbles ◽  
Augmented Dickey Fuller ◽  
Multiple Bubbles ◽  
Market Fundamentals ◽  
The Stability

In this paper, we employ the Generalized Supremum Augmented Dickey-Fuller test in order to identify the existence of multiple bubbles in natural rubber. This approach is practical for the using of time series and identifies the beginning and end points of multiple bubbles. The results reveal that there are five bubbles, where exist the divergences between natural rubber prices and their basic values on account of market fundamentals. The five bubbles are related to imbalance between supply and demand, inefficiencies of smallholders market, oil prices, exchange rate and climatic changes through analyses. Thus, the corresponding authorities are supposed to identify bubbles and consider their evolutions, which is beneficial to the stability of natural rubber price.

Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 2636-2640 ◽  
Author(s):  
Jian Hua Zhang ◽  
Fan Tao Kong ◽  
Jian Zhai Wu ◽  
Meng Shuai Zhu ◽  
Ke Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Agricultural Production ◽  
Supply And Demand ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Agricultural Products ◽  
Price Model ◽  
Agricultural Prices

Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.

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