Multi-layer perceptron training algorithms for pattern recognition of myoelectric signals

2013 ◽  
Author(s):  
Le M.D. Khong ◽  
Timothy J. Gale ◽  
Danchi Jiang ◽  
Jan C. Olivier ◽  
Max Ortiz-Catalan
Keyword(s):  
Pattern Recognition ◽  
Training Algorithms ◽  
Multi Layer Perceptron ◽  
Myoelectric Signals

Multi-Layer Perceptron Pattern Recognition of Handwriting Ink Based on PLS-DA Raman Spectral Feature Extraction

2021 ◽  
Vol 58 (1) ◽  
pp. 0130002
Author(s):  
王晓宾 Wang Xiaobin ◽  
马枭 Ma Xiao ◽  
杨蕾 Yang Lei ◽  
李春宇 Li Chunyu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Spectral Feature ◽  
Multi Layer Perceptron ◽  
Raman Spectral

scholarly journals Multi layer perceptron model in pattern recognition of surface parameters during pre-monsoon thunderstorm

MAUSAM ◽  
2022 ◽  
Vol 53 (4) ◽  
pp. 417-424
Author(s):  
SUTAPA CHAUDHURI ◽  
SURAJIT CHATTOPADHYAY
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Surface Temperature ◽  
Surface Pressure ◽  
Dew Point ◽  
Multi Layer Perceptron ◽  
Surface Parameters ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Definite Pattern

The concept of Multi Layer Perceptron and Fuzzy logic is introduced in this paper to recognize the pattern of surface parameters pertaining to forecast the occurrence of pre-monsoon thunderstorms over Kolkata (22 ° 32¢ , 88 ° 20¢ ).   The results reveal that surface temperature fluctuates significantly from Fuzzy Multi Layer Perceptron (FMLP) model values on thunderstorm days whereas on non-thunderstorm days FMLP model fits well with the surface temperature.   The results further indicate that no definite pattern could be made available with surface dew point temperature and surface pressure that can help in forecasting the occurrence of these storms.

Predicting thrust of aircraft using artificial neural networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatma Yildirim Dalkiran ◽  
Mustafa Toraman
Keyword(s):  
Flight Control ◽  
Data Sets ◽  
Training Algorithms ◽  
Control Parameters ◽  
Multi Layer Perceptron ◽  
Ann Model ◽  
Training Process ◽  
Content Type ◽  
Minimum Error ◽  
Ann Models

Purpose The purpose of this study is to make artificial neural network (ANN)-based prediction about thrust using the flight control parameters of aircrafts. Design/methodology/approach In today’s transportation, airplanes have an important place because of their safety, quality and speed. One of the most important parameters affecting the secure flying of aircrafts is the thrust value of aircraft engines. Determining the optimum thrust value should be investigated. If thrust value is less than optimum level, the flight safety runs a risk. Otherwise, fuel consumption goes high and some unwanted vibrations occur that cause uncomfortable flight. In this study, multi-layer perceptron ANNs, which are one of the intelligent optimization methods and frequently used in the literature, are preferred to predict the optimum thrust value during take-off, cruise and landing. The actual flight data, which is taken from the black box of an Airbus A319 aircraft, is used to train ANN models using back propagation algorithms. Velocity, altitude and ambient temperature values of the aircraft are selected as inputs and the thrust value is selected as output. During the training process of ANN, eight different training algorithms with different structures are used to figure out optimum ANN model with minimum error. Findings Different ANN models were trained using eight different training algorithms. The ANN model with minimum error has multi-layer perceptron structure, which is trained using Levenberg–Marquardt (LM) algorithm. Research limitations/implications To obtain the ANN structure with minimum error training, process takes more than a day depending on the capacity of a computer for LM training algorithm. But after training process, the trained ANN model produces sufficient output in a few milliseconds. Practical implications Totally 15,670 input-output data sets are obtained from an Airbus A319 aircraft. 12,889 of them are used as training data and the rest of the data sets, selected randomly are used as test data. Test data sets are never used in training phase, and the obtained results show that the ANN model successfully predicts thrust value using unseen input data. Social implications The ANN could be used as an alternative method to predict other flight control parameters of aircrafts. Originality/value To the best of authors’ knowledge, this study is the first example in literature to predict the thrust value of the aircraft using ANN.

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