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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Analysing rounding data using radial basis function neural networks model

Triastuti Sugiyarto, Endang January 2007 (has links)
Unspecified counting practices used in a data collection may create rounding to certain ‘based’ number that can have serious consequences on data quality. Statistical methods for analysing missing data are commonly used to deal with the issue but it could actually aggravate the problem. Rounded data are not missing data, instead some observations were just systematically lumped to certain based numbers reflecting the rounding process or counting behaviour. A new method to analyse rounded data would therefore be academically valuable. The neural network model developed in this study fills the gap and serves the purpose by complementing and enhancing the conventional statistical methods. The model detects, analyses, and quantifies the existence of periodic structures in a data set because of rounding. The robustness of the model is examined using simulated data sets containing specific rounding numbers of different levels. The model is also subjected to theoretical and numerical tests to confirm its validity before being used on real applications. Overall, the model performs very well making it suitable for many applications. The assessment results show the importance of using the right best fit in rounding detection. The detection power and cut-off point estimation also depend on data distribution and rounding based numbers. Detecting rounding of prime numbers is easier than non-prime numbers due to the unique characteristics of the former. The bigger the number, the easier is the detection. This is in a complete contrast with non-prime numbers, where the bigger the number, the more will be the “factor” numbers distracting rounding detection. Using uniform best fit on uniform data produces the best result and lowest cut-off point. The consequence of using a wrong best fit on uniform data is however also the worst. The model performs best on data containing 10-40% rounding levels as less or more rounding levels produce unclear rounding pattern or distort the rounding detection, respectively. The modulo-test method also suffers the same problem. Real data applications on religious census data confirms the modulo-test finding that the data contains rounding base 5, while applications on cigarettes smoked and alcohol consumed data show good detection results. The cigarettes data seem to contain rounding base 5, while alcohol consumption data indicate no rounding patterns that may be attributed to the ways the two data were collected. The modelling applications can be extended to other areas in which rounding is common and can have significant consequences. The modelling development can he refined to include data-smoothing process and to make it user friendly as an online modelling tool. This will maximize the model’s potential use
2

Identification of chemical species using artificial intelligence to interpret optical emission spectra

Ampratwum, Cecilia S. January 1999 (has links)
The nonlinear modeling capabilities of artificial neural networks (ANN’s) are renowned in the field of artificial intelligence (Al) for capturing knowledge that can be very difficult to understand otherwise. Their ability to be trained on representative data within a particular problem domain and generalise over a set of data make them efficient predictive models. One problem domain that contains complex data that would benefit from the predictive capabilities of ANN’s is that of optical emission spectra (OES). OES is an important diagnostic for monitoring plasma species within plasma processing. Normally, OES spectral interpretation requires significant prior expertise from a spectroscopist. One way of alleviating this intensive demand in order to quickly interpret OES spectra is to interpret the data using an intelligent pattern recognition technique like ANN’s. This thesis investigates and presents MLP ANN models that can successfully classify chemical species within OES spectral patterns. The primary contribution of the thesis is the creation of deployable ANN species models that can predict OES spectral line sizes directly from six controllable input process parameters; and the implementation of a novel rule extraction procedure to relate the real multi-output values of the spectral line sizes to individual input process parameters. Not only are the trained species models excellent in their predictive capability, but they also provide the foundation for extracting comprehensible rules. A secondary contribution made by this thesis is to present an adapted fuzzy rule extraction system that attaches a quantitative measure of confidence to individual rules. The most significant contribution to the field of Al that is generated from the work presented in the thesis is the fact that the rule extraction procedure utilises predictive ANN species models that employ real continuously valued multi-output data. This is an improvement on rule extraction from trained networks that normally focus on discrete binary outputs
3

Development of an artificial neural network model to predict expert judgement of leather handle from instrumentally measured parameters

Wang, Yijun January 2009 (has links)
Leather is a widely used material whose handling character is still assessed manually by experienced people in the leather industry. The aim of this study was to provide a new approach to such characterisation by developing Artificial Neural Network models to investigate the relationship between the subjective assessment of leather handle and its measureable physical characteristics. Two collections of commercial leather samples provided by TFL and PITTARDS were studied in this project. While the handle of the TFL collection covered a varied range, the PITTARDS collection was all relatively soft leather and with less difference within the collection. Descriptive Sensory Analysis was used to identify and quantify the subjective assessment of leather handle. A panel constituted of leather experts was organised and trained to: 1) define attributes describing leather handle; 2) assess specific leather handle by responding to questionnaires seeking information about the above attributes. According to the analysis of the raw data and the assessment observation, the attributes that should be used for training the artificial network models were "stiff", "empty", "smooth", "firm", "high density" and "elastic". Various physical measurements relating to leather handle were carried out as follows: standard leather thickness, apparent density, thickness with 1 gram load and 2 gram load, resistance to compression, resistance to stretching, surface friction, modified vertical loop deformation, drooping angle and BLC softness. The parameters from each measurement were all scaled on range 0 to 1 before being fed into network models. Artificial neural networks were developed through learning from the TFL examples and then tested on the PITTARDS collection. In the training stage, parameters from physical measurements and attribute gradings provided by descriptive sensory analysis were fed into the networks as input and desired output respectively. In the testing stage, physical measurement parameters were input to the trained network and the output of the network, which was the prediction of the leather handle, was compared with the gradings given by the panel. The testing results showed that the neural network models developed were able to judge the handle of a newly presented leather as well as an expert. Statistical methods were explored in the development of artificial neural network models. Principal Component Analysis was used to classify the attributes of leather handle and demonstrated that the predominant and most representative attributes out of the six attributes were "stiff", "empty" and "smooth". A network model called physical2panel, predicting the above three attributes from three physical parameters was built up by adopting a novel pruning method termed "Double-Threshold" which was used to decide the irrelevance of an input to a model. This pruning method was based on Bayesian methodology and implemented by comparing the overall connection weight of each input to each output with the limitation of two thresholds. The pruning results revealed that among the sixteen physical parameters, only three of them, - the reading from BLC softness guage, the compression secant modulus and the leather thickness measured under 1 gram load were important to the model. Another network model, termed panel2panel, that predicts the other three attributes "firm", "high density" and "elastic" from the prediction of the model physical2panel was developed and also proved to work as well as a leather expert panel. The conception of a 3D handle space was explored and shown to be a powerful means of demonstrating the findings.

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