Multi-layer perceptron artificial neural network (MLP-ANN) prediction of biomass higher heating value (HHV) using combined biomass proximate and ultimate analysis data

2021 ◽  
Author(s):  
Joshua O. Ighalo ◽  
Chinenye Adaobi Igwegbe ◽  
Adewale George Adeniyi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analysis Data ◽  
Multi Layer Perceptron ◽  
Heating Value ◽  
Ultimate Analysis ◽  
Proximate And Ultimate Analysis ◽  
Artificial Neural

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

2020 ◽  
pp. 489-510
Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Multi Layer Perceptron ◽  
Sar Images ◽  
Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

scholarly journals Artificial Neural Network (ANN) based Object Recognition Using Multiple Feature Sets

2011 ◽  
pp. 32-36
Author(s):  
Manami Barthakur ◽  
Tapashi Thakuria ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Multi Layer Perceptron ◽  
Multiple Features ◽  
Error Back Propagation ◽  
Multiple Feature ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.

scholarly journals Penggunaan Artificial Neural Network pada Sinyal Elektrokardiogram untuk Mendeteksi Penyakit Jantung Aritmia Supraventrikular

2021 ◽  
Vol 13 (1) ◽  
pp. 14-23
Author(s):  
Niendy Alexandra Yosephine ◽  
Ratnadewi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multi Layer Perceptron ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Aritmia supraventrikular adalah salah satu jenis gangguan irama jantung yang bersumber dari nodus AV atau impuls listrik di atrium, dengan keadaan jantung yang berdetak lebih cepat dari normal. Aritmia supraventrikular masih dapat diobati dengan obat tertentu sehingga akan sangat membantu penderita bila penyakit tersebut terdeteksi lebih awal. Pemrosesan sinyal elektrokardiogram (EKG) terhadap penyakit Aritmia supraventrikular perlu dilakukan untuk mendeteksi lebih awal adanya permasalahan pada jantung khususnya penyakit aritmia supraventrikular. Artificial Neural Network (ANN) digunakan untuk mendeteksi penyakit jantung Aritmia supraventrikular dan jantung normal karena kelebihannya dalam mengklasifikasi suatu data dengan tepat, proses yang singkat dan pengelolaan mandiri. Hasil akhir dalam penelitian ini didapatkan nilai tertinggi dalam keberhasilan mengklasifikasi berasal dari struktur algoritma Multi-Layer Perceptron. Nilai akurasi hasil pengujian tertinggi berasal dari metode pelatihan menggunakan Resilient Backpropagation yaitu sebesar 87,5%. Nilai specificity hasil pengujian tertinggi berasal dari metode pelatihan menggunakan Levenberg Marquard sebesar 83,3%. Nilai sensitivity hasil pengujian tertinggi berasal dari metode pelatihan menggunakan Resilient Backpropagation yaitu sebesar 100%.

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