Artificial Neural Network Model For Wind Mill

Utilization of wind energy sources provides advantages in terms of being environmentally friendly, and it can be energy source is realible. The analysis of wind mill control using Neural Network model for Uncertain Variables or abbreviated as the VTP model is expected to provide a solution in solving the windmill control case. And the Neural Network model for Uncertain Variables uses probability techniques, degree of membership, logical OR function, linear programming and euclidean distance to reduce the learning process In this research, wind mill control uses variable air pressure and duration of sunshine to determine whether the wind mill is moving or not. Finally, this research tries to analyze windmill control, which in the future is expected to produce a smart wind mill control system. And the Neural Network model for Uncertain Variables can be used to control windmills with the different of input data

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PERANCANGAN MODEL UNTUK PERAMALAN TINGKAT PERTUMBUHAN PEMBIAYAAN MELALUI METODE JARINGAN SARAF TIRUAN DI BNI SYARIAH

Jurnal Ekonomi Pembangunan STIE Muhammadiyah Palopo ◽

10.35906/jep01.v6i2.587 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Orfyanny S Themba ◽

Susianah Mokhtar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Data Bank ◽

Forecasting Accuracy ◽

The Neural Network ◽

Forecasting Method ◽

Artificial Neural

ABSTRAKTren perkembangan pembiayaan di Indonesia mulai meningkat namun cenderung melambat dari tahun ke tahun. Peramalan pertumbuhan pembiayaan pada bank syariah menjadi hal yang menarik karena naik turunnya pembiayaan akan berdampak pada perekonomian Indonesia. Tujuan dari penelitian ini melakukan peramalan pertumbuhan pembiayaan dalam jangka waktu setahun melalui metode Jaringan Saraf Tiruan pada data Bank BNI Syariah dari tahun 2015 sampai dengan 2019. Hasil dari peramalan diharapkan memberi informasi bagi bank untuk menunjang pengambilan keputusan dan menyiapkan strategi meningkatkan pembiayaan sehingga semakin besar laba yang akan diperoleh. Model peramalan dibuat berdasarkan metode peramalan dan ditujukan untuk digunakan pada aplikasi peramalan pembiayaan. Model Jaringan Saraf Tiruan memiliki nilai akurasi peramalan yang tinggi karena memiliki nilai error RMSE, MAPE yang minimum. Dari hasil peramalan menggunakan model Jaringan Saraf Tiruan menunjukkan terjadi peningkatan pembiayaan pada setiap bulannya untuk akad murabahah, mudharabah, musyarakah dan qardh. Hanya pembiayaan yang menggunakan ijarah yang mengalami penurunan drastis dibanding tahun-tahun sebelumnya. Pembiayaan murabahah masih tetap mendominasi dibanding akad mudharabah, musyarakah, qardh dan ijarah selama tahun 2020 Kata Kunci: Jaringan Saraf Tiruan ;PembiayaanABSTRACT Trend of financing development in Indonesia is starting to increase but tends to slow down from year to year. It is interesting to forecast the growth of financing in Islamic banks because the up and down of financing will have an impact on the Indonesian economy. The purpose of this study to forecast financing growth within a year through the Neural Network method on BNI Syariah Bank data from 2015 to 2019. The results of the forecast are expected to provide information for banks to support decision making and prepare strategies to increase financing so that greater profits that will be obtained. The forecasting model is made based on the forecasting method and is intended for use in financing forecasting applications. The Artificial Neural Network Model has a high value of forecasting accuracy because it has a minimum error value of RMSE, MAPE. The results of forecasting using the Artificial Neural Network model show an increase in financing every month for murabahah, mudharabah, musyarakah and qardh contracts. Only financing using ijarah has experienced a drastic decline compared to previous years. Murabahah financing still dominates over the mudharabah, musyarakah, qardh and ijarah contracts during 2020Keyword: Arificial Neural Network ;Financing

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Snack Texture Estimation System Using a Simple Equipment and Neural Network Model

Future Internet ◽

10.3390/fi11030068 ◽

2019 ◽

Vol 11 (3) ◽

pp. 68 ◽

Cited By ~ 2

Author(s):

Shigeru Kato ◽

Naoki Wada ◽

Ryuji Ito ◽

Takaya Shiozaki ◽

Yudai Nishiyama ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Food Texture ◽

The Neural Network ◽

Equipment Unit ◽

Estimation System

Texture evaluation is manually performed in general, and such analytical tasks can get cumbersome. In this regard, a neural network model is employed in this study. This paper describes a system that can estimate the food texture of snacks. The system comprises a simple equipment unit and an artificial neural network model. The equipment simultaneously examines the load and sound when a snack is pressed. The neural network model analyzes the load change and sound signals and then outputs a numerical value within the range (0,1) to express the level of textures such as “crunchiness” and “crispness”. Experimental results validate the model’s capacity to output moderate texture values of the snacks. In addition, we applied the convolutional neural network (CNN) model to classify snacks and the capability of the CNN model for texture estimation is discussed.

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Using Models of Collective Neural Networks for Classification of the Input Data Applying Simple Voting

The Journal of Social Sciences Research ◽

10.32861/jssr.spi5.333.339 ◽

2018 ◽

pp. 333-339

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Model ◽

Neural Network Model ◽

Network Architecture ◽

Input Data ◽

Bootstrap Method ◽

Classification Models ◽

The Neural Network ◽

Voting Method

This paper deals with the use of neural networks in binary classification problems based on the simple voting method. It specifies that the accuracy of the neural network classification depends both on the choice of the network architecture and on the partitioning of data into training and test sets. It is noted that the process of building a neural network model is probabilistic in nature. To eliminate this drawback and improve the accuracy of classification, the need to combine several models in the form of a collective of neural networks is actualized. To build such a model, it is proposed to use the 0.632-bootstrap method. To aggregate individual solutions formed at the output of each neural network, it is proposed to use a single-choice simple voting. The choice of the model structure in the form of a single-layer Perceptron is justified, and its mathematical model is presented. Using the evaluation data of the functional state of a drunk human as an example, the results of an experimental assessment of the bootstrap error and the accuracy of the neural network model are presented. It is concluded that it is possible to achieve a higher accuracy of classification based on the neural network model when aggregating the results of all bootstrap models using the simple voting method. The accuracy of the constructed model is compared with the accuracy of other classification models. The accuracy of the constructed model was 96.7%, which on average exceeded the accuracy of other classification models by 6.6%. Thus, the neural network collective model is an effective tool for classifying input data using the simple voting method.

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