000 02725nam a2200253 a 4500
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005 20251114204532.0
008 121122t2012 my d m 000 0 eng d
020 _aTHE0007118(Local)
039 9 _a201906131146
_bhanafiah
_c201212051020
_dhuda
_c201212051018
_dhuda
_y201211221237
_zhuda
040 _aUMP
090 _aTL152.8 .A89 2012 rs Bc.
100 0 _aHusnul ‘Asyiyyah Mohamad @ Awang
245 1 0 _aDevelopment of vision autonomous guided vehicle behaviour using neural network /
_cHusnul ‘Asyiyyah Mohamad @ Awang
260 _aKuantan, Pahang :
_bUMP,
_c2012
300 _axiv, 60 p. ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper (Bachelor of Manufacturing Engineering) -- Universiti Malaysia Pahang - 2012
504 _aIncludes bibliographical references
520 3 _aThis project is motivated by an interest in promoting the use of artificial neural network in manufacturing. Automated guided vehicle (AGV) is used in advanced manufacturing system that can help to reduce cost and increase efficiency. The application of neural network in the AGV is to help in increasing the AGVs performance and efficiency. The objectives of this project are to develop a line recognition algorithm for automated guided vehicle and to understand two types of neural networks that can be use in manufacturing. The types of guidelines used in this project are straight guideline, turn right guideline, turn left guideline and stop guideline. The line recognition algorithm involved the pre-processing images of the guideline captured by a camera and extracts the feature of the images by using first order statistics to calculate the values of mean, variance, skewness and kurtosis and train the image recognition by using neural networks. Neural network process involved setup the two types of neural network, trained and tested the network and compared the result. There are two types of neural network that used in this project namely, Feedforward Backpropagation and Radial Basis. In Feedforward Backpropagation Network the parameter involves are transfer function and number of neurons. Mean Squared Error (MSE) is used as performance function. Radial Basis Network with spread constant one give significantly better performance compared to Feedforward Backpropagation Network. It produced much lower error compared to Feedforward Backpropagation Network. This project used MATLAB software which able to perform image processing tasks, train and simulate neural networks.
650 0 _aAutomated guided vehicle systems
650 0 _aNeural networks (Computer science)
999 _aVIRTUA40
_c3712
_d3718
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*9992