Automated Evaluation of Kikuchi Patterns by Means of Radon and Fast Fourier Transformation, and Verification by an Artificial Neural Network

2003 ◽  
Vol 5 (8) ◽  
pp. 601-606 ◽  
Author(s):  
R.A. Schwarzer ◽  
J. Sukkau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transformation ◽  
Fast Fourier Transformation ◽  
Automated Evaluation ◽  
Artificial Neural

scholarly journals STUDI KOMPARASI PREDIKSI CURAH HUJAN METODE FAST FOURIER TRANSFORMATION (FFT), AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN ARTIFICIAL NEURAL NETWORK (ANN)

2015 ◽  
Vol 35 (02) ◽  
pp. 241
Author(s):  
Dyah Susilokarti ◽  
Sigit Supadmo Arif ◽  
Sahid Susanto ◽  
Lilik Sutiarso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Fast Fourier Transformation ◽  
Autoregressive Integrated Moving Average ◽  
Precipitation Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Optimum climate condition and water availability are essential to support strategic venue and time for plants to grow and produce.  Precipitation prediction is needed to determine how much precipitation will provide water for plants on each stage of growth. Nowadays, the high variability of precipitation calls for a prediction model that will accurately foreseethe precipitation condition in the future. The prediction conducted is based on time-series data analysis. The research aims to comparethe effectiveness of three precipitation prediction methods, which are Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN).  Their respective performances are determined by their Mean Square Error (MSE) values.  Methods with highest correlation values and lowest MSE shows the best performance. The MSE result for FFT is 14,92; ARIMA is 17,49; and  ANN is 0,07. This research concluded that Artificial Neural Network (ANN) method showed best performance compare to the other two because it had produced a prediction with the lowest MSE value.Keywords: Precipitation prediction, Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average ABSTRAKKondisi iklim dan ketersediaan air yang optimal bagi pertumbuhan dan perkembangan tanaman sangat diperlukan dalam upaya mendukung strategi budidaya tanaman sesuai ruang dan waktu. Prediksi curah hujan sangat diperlukan untuk untuk mengetahui sejauh mana curah hujan dapat memenuhi kebutuhan air pada setiap tahap pertumbuhantanaman. Variabilitas curah hujan yang tinggi saat ini, membutuhkan pemodelan yang dapat memprediksi secara akurat bagaimana kondisi curah hujan dimasa yang akan datang. Prediksi yang dilakukan adalah prediksi berdasarkan urutan waktu ().  Tujuan dari penelitian ini adalah untuk membandingkan akurasi prediksi curah hujan antara metode  (FFT),  (ARIMA) dan (ANN). Kinerja ketiga metode yang digunakan dilihat dari nilai  (MSE). Metode dengan nilai korelasi tertinggi dan nilai MSE terkecil menunjukkan kinerja terbaik. Hasil penelitan untuk FFT diperoleh nilai MSE = 14,92, ARIMA = 17,49 sedangkan ANN = 0,07. Ini menunjukkan bahwa metode   (ANN) menunjukkan kinerja yang paling baik diantara dua metode lainnya karena menghasilkan prediksi yangmempunyai nilai MSE terkecil.Kata kunci: Prediksi curah hujan,FFT, ARIMA dan ANN 

METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW

2020 ◽  
Vol 14 (2) ◽  
pp. 48-53
Author(s):  
Herlina Jayadianti ◽  
Tedy Agung Cahyadi ◽  
Nur Ali Amri ◽  
Muhammad Fathurrahman Pitayandanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Fast Fourier Transformation ◽  
Feed Forward Neural Network ◽  
Inference System ◽  
Artificial Neural

Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR-Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), dan Artificial Neural Network-Fuzzy (ANN-Fuzzy). Hasil dari review menyimpulkan bahwa model Artificial Neural Network memiliki beberapa kelebihan dibandingkan dengan metode yang lain, yakni ANN mampu memberikan hasil yang dapat mengenali pola-pola dengan baik dan mudah dikembangkan menjadi bermacam-macam variasi sesuai dengan permasalahan maupun parameter yang ada, sehingga ANN direkomendasikan untuk perhitungan prediksi hujan. Abstract - Various kinds of research have been carried out to find accurate models to predict rainfall in various fields, so the research that has been done previously was reviewed again to help the drainage process in mining companies. The review is done by comparing the results of each model that has been conducted in several previous studies. This research used quantitative methods. Models compared in this study include the Fuzzy model, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR -Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network-Fuzzy (ANN-Fuzzy). The results of the review concluded that the Artificial Neural Network model has several advantages compared to other methods, namely ANN is able to provide results that can recognize patterns well and easily developed into a variety of variations in accordance with existing problems and parameters, so ANN is recommended for rain prediction calculation.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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