scholarly journals Perbandingan Antara Multi Atribut Seismik Regresi Linier dan Multi Atribut Seismik Probabilistic Neural Network Untuk Estimasi Porositas Reservoir Batu Pasir Pada Lapangan Minyak Teapot Dome

2017 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Zulfani Aziz ◽  
Ari Setiawan
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Teapot Dome

Dalam eksplorasi minyak bumi, informasi tentang batuan di bawah permukaan bumi sangat diperlukan untuk mengetahui zona reservoir target eksplorasi yang salah satunya adalah porositas batuan. Untuk mendapatkan informasi porositas batuan digunakan metode multiatribut seismik yang dapat mengestimasi porositas dari atribut-atribut seismik. Metode multiatribut seismik memiliki dua jenis yaitu regresi linier dan probabilistic neural network (PNN). Penelitian ini bertujuan untuk mengetahui metode multiatribut seismik mana yang memberikan hasil yang lebih baik dalam mengestimasi nilai porositas batu pasir di lapangan minyak Teapot Dome. Pada penelitian ini digunakan tiga atribut seismik yaitu atribut impedansi akustik, integrate, dan amplitude weighted frequency. Multiatribut seismik regresi linier menganggap hubungan ketiga atribut seismik dan porositas adalah linier sedangkan multiatribut seismik probabilistic neural network menganggap hubungannya non linier. Hasil penelitian menunjukan bahwa metode multiatribut seismik regresi linier memberikan estimasi porositas dengan nilai korelasi 0,701 dan validasi 0,649, sedangkan metode multiatribut seismik probabilistic neural network memberikan estimasi porositas lebih baik dengan nilai korelasi 0,920 dan validasi 0,683. Hasil lain juga memperlihatkan bahwa bentuk kurva log porositas hasil estimasi probabilistic neural network lebih cocok dengan log porositas asli dibandingkan log porositas hasil estimasi regresi linier. 

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

scholarly journals Study on channel prediction for automated guided vehicle using a probabilistic neural network

2019 ◽  
Vol 8 (8) ◽  
pp. 311-317 ◽  
Author(s):  
Julian Webber ◽  
Norisato Suga ◽  
Abolfazl Mehbodniya ◽  
Kazuto Yano ◽  
Yoshinori Suzuki
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Automated Guided Vehicle ◽  
Channel Prediction

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection

2018 ◽  
Vol 108 ◽  
pp. 339-354 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Kalpana V. ◽  
Kannan Kirthivasan ◽  
Shankar Sriram V.S.
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Service Selection ◽  
Cloud Service ◽  
Trust Prediction ◽  
Prediction Approach

Probabilistic neural network playing and learning Tic–Tac–Toe

2005 ◽  
Vol 26 (12) ◽  
pp. 1866-1873 ◽  
Author(s):  
Jiří Grim ◽  
Petr Somol ◽  
Pavel Pudil
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network
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