Dissertations / Theses: 'Stochastic systems; neural networks; computer science'

1

Chan, Wing-chi. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22925843.

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陳穎志 and Wing-chi Chan. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241475.

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Malmgren, Henrik. "Revision of an artificial neural network enabling industrial sorting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392690.

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Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system.The findings indicate that it's important to use spatial variety dropout regularization for high resolution image inputs, and use an optimizer configuration with good convergence properties. The findings also demonstrate examples of ensemble classifiers being effectively consolidated into unified models using the distillation technique. An analogue arrangement with optimization against multiple output targets, incorporating additional information, showed accuracy gains comparable to ensembling. For use of the classifier on test data with statistics different than those of the dataset, results indicate that augmentation of the input data during classifier creation helps performance, but would, in the current case, likely need to be guided by information about the distribution shift to have sufficiently positive impact to enable a practical application. I suggest, for future development, updated architectures, automated hyperparameter search and leveraging the bountiful unlabeled data potentially available from production lines.

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Morphet, Steven Brian Işık Can. "Modeling neural networks via linguistically interpretable fuzzy inference systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2004. http://wwwlib.umi.com/cr/syr/main.

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Wheeler, Diek Winters. "Nonlinear behavior in small neural systems /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Shah, Hemal Vinodchandra 1967. "Performance evaluation of manufacturing systems using stochastic activity networks." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/278068.

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In this thesis, Stochastic Activity Networks (SANs), which are an extension to the Petri Nets, are used for performance evaluation of manufacturing systems. Using our formalism, a manufacturing system is hierarchically represented in three different layers: the manufacturing flow layer, the control layer and the network layer. SAN models are constructed for each of these layers. To simplify the understanding of the manufacturing flow, a new graphical representation, the Manufacturing Flow Network (MFN) has been developed. Conversion of MFN into SAN models simplifies the modeling of manufacturing flow layer. When MFN at the product level is very complex, a decomposition technique is applied to reduce complexity of the model under specific conditions. The accuracy of this technique is shown for specific conditions. Finally, a performance evaluation of a sample manufacturing system is shown, using the simulation for solution of the model. Performance variables of interest such as machine utilization, machine availability and operation queue length are discussed.

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Orr, Genevieve Beth. "Dynamics and algorithms for stochastic search /." Full text open access at:, 1995. http://content.ohsu.edu/u?/etd,197.

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Park, Dong Chul. "Identification of stationary/nonstationary systems using artificial neural networks /." Thesis, Connect to this title online; UW restricted, 1990. http://hdl.handle.net/1773/5822.

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林誠 and Shing Lam. "Stability of neural network control systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31214265.

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Lam, Shing. "Stability of neural network control systems /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1859797X.

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Laughton, Stephen Nicholas. "Dynamics of neural networks and disordered spin systems." Thesis, University of Oxford, 1995. http://ora.ox.ac.uk/objects/uuid:5531cef6-4682-4750-9c5c-cb69e5e72d64.

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I obtain a number of results for the dynamics of several disordered spin systems, of successively greater complexity. I commence with the generalised Hopfield model trained with an intensive number of patterns, where in the thermodynamic limit macroscopic, deterministic equations of motion can be derived exactly for both the synchronous discrete time and asynchronous continuous time dynamics. I show that for symmetric embedding matrices Lyapunov functions exist at the macroscopic level of description in terms of pattern overlaps. I then show that for asymmetric embedding matrices several types of bifurcation phenomena to complex non-transient dynamics occur, even in this simplest model. Extending a recent result of Coolen and Sherrington, I show how the dynamics of the generalised Hopfield model trained with extensively many patterns and non-trivial embedding matrix can be described by the evolution of a small number of overlaps and the disordered contribution to the 'energy', upon calculation of a noise distribution by the replica method. The evaluation of the noise distribution requires two key assumptions: that the flow equations are self averaging, and that equipartitioning of probability occurs within the macroscopic sub-shells of the ensemble. This method is inexact on intermediate time scales, due to the microscopic information integrated out in order to derive a closed set of equations. I then show how this theory can be improved in a systematic manner by introducing an order parameter function - the joint distribution of spins and local alignment fields, which evolves in time deterministically, according to a driven diffusion type equation. I show how the coefficients in this equation can be evaluated for the generalised Sherrington-Kirkpatrick model, both within the replica symmetric ansatz, and using Parisi's ultrametric ansatz for the replica matrices, upon making once again the two key assumptions (self averaging and equipartitioning). Since the order parameter is now a continuous function, however, the assumption of equipartitioning within the macroscopic sub-shells is much less restricting.

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Frayman, Yakov, and mikewood@deakin edu au. "Fuzzy neural networks for control of dynamic systems." Deakin University. School of Computing and Mathematics, 1999. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051017.145550.

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This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

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Ismael, Ali. "Neural adaptive control systems /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9901244.

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Ramani, Vipin. "Reconfigurable control using polynomial neural networks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13297.

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Pirovolou, Dimitrios K. "The tracking problem using fuzzy neural networks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14824.

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Moharari, Nader S. "An electric load forecasting approach using expert systems and artificial neural networks." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/13757.

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Leung, Yiu-cheung. "A reconfigurable neural network for industrial sensory systems /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23234441.

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McFarland, Michael Bryan. "Adaptive nonlinear control of missiles using neural networks." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/13283.

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Tien, Fang-Chih. "Using neural networks for three-dimensional measurement in stereo vision systems /." free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9720552.

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Havstad, Alexander. "Image quality assessment using artificial neural networks." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2005. https://ro.ecu.edu.au/theses/664.

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Kwok, Terence 1973. "Neural networks with nonlinear system dynamics for combinatorial optimization." Monash University, School of Business Systems, 2001. http://arrow.monash.edu.au/hdl/1959.1/8928.

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22

Mazumdar, Sanjay Kumar. "Adaptive control of nonlinear systems using neural networks /." Title page, contents and abstract only, 1995. http://web4.library.adelaide.edu.au/theses/09PH/09phm476.pdf.

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Rabi, Gihad. "Visual speech recognition by recurrent neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0010/MQ36169.pdf.

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梁耀祥 and Yiu-cheung Leung. "A reconfigurable neural network for industrial sensory systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31224751.

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Tran, Michael. "Neural network identification of quarter-car passive and active suspension systems." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09292009-020158/.

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Bean, Ralph. "Vibrational control of chaos in artificial neural networks /." Online version of thesis, 2009. http://hdl.handle.net/1850/10645.

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Turner, Kevin Michael. "Estimation of Ocean Water Chlorophyll-A Concentration Using Fuzzy C-Means Clustering and Artificial Neural Networks." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/TurnerKM2007.pdf.

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Gottschling, Andreas Peter. "Three essays in neural networks and financial prediction /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9728773.

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Maroli, John Michael. "Generating Comprehensible Equations from Unknown Discrete Dynamical Systems Using Neural Networks." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574760744876635.

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He, Changhua, and 何昌華. "Resource management for handoff control in wireless/mobile networks using artificial neural networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226000.

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Chan, Leonard. "Implementation of CMAC as a neural network controller on mechanical systems /." [St. Lucia, Qld.], 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17135.pdf.

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Phillips, Vincent C. "A Model Of Visual Recognition Implemented Using Neural Networks." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1994. https://ro.ecu.edu.au/theses/1472.

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The ability to recognise and classify objects in the environment is an important property of biological vision. It is highly desirable that artificial vision systems also have this ability. This thesis documents research into the use of artificial neural networks to implement a prototype model of visual object recognition. The prototype model, describing a computtional architecture, is derived from relevant physiological and psychological data, and attempts to resolve the use of structural decomposition and invariant feature detection. To validate the research a partial implementation of the model has been constructed using multiple neural networks. A linear feed-forward network performs pre-procesing after being trained to approximate a conventional statistical data compression algorithm. The output of this pre-processing forms a feature vector that is categorised using an Adaptive Resonance Theory network capable of recognising arbitrary analog patterns. The implementation has been applied to the task of recognising static images of human faces. Experimental results show that the implementation is able to achieve a 100% successful recognition rate with performance that degrades gracefully. The implentation is robust against facial changes minor occlusions and it is flexible enough to categorise data from any domain.

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Alahakoon, Lakpriya Damminda 1968. "Data mining with structure adapting neural networks." Monash University, School of Computer Science and Software Engineering, 2000. http://arrow.monash.edu.au/hdl/1959.1/7987.

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Strömberg, Lucas. "Optimizing Convolutional Neural Networks for Inference on Embedded Systems." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444802.

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Convolutional neural networks (CNN) are state of the art machine learning models used for various computer vision problems, such as image recognition. As these networks normally need a vast amount of parameters they can be computationally expensive, which complicates deployment on embedded hardware, especially if there are contraints on for instance latency, memory or power consumption. This thesis examines the CNN optimization methods pruning and quantization, in order to explore how they affect not only model accuracy, but also possible inference latency speedup. Four baseline CNN models, based on popular and relevant architectures, were implemented and trained on the CIFAR-10 dataset. The networks were then quantized or pruned for various optimization parameters. All models can be successfully quantized to both 5-bit weights and activations, or pruned with 70% sparsity without any substantial effect on accuracy. The larger baseline models are generally more robust and can be quantized more aggressively, however they are also more sensitive to low-bit activations. Moreover, for 8-bit integer quantization the networks were implemented on an ARM Cortex-A72 microprocessor, where inference latency was studied. These fixed-point models achieves up to 5.5x inference speedup on the ARM processor, compared to the 32-bit floating-point baselines. The larger models gain more speedup from quantization than the smaller ones. While the results are not necessarily generalizable to different CNN architectures or datasets, the valuable insights obtained in this thesis can be used as starting points for further investigations in model optimization and possible effects on accuracy and embedded inference latency.

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Asthorsson, Axel. "Simulation meta-modeling of complex industrial production systems using neural networks." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1036.

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Simulations are widely used for analysis and design of complex systems. Real-world complex systems are often too complex to be expressed with tractable mathematical formulations. Therefore simulations are often used instead of mathematical formulations because of their flexibility and ability to model real-world complex systems in some detail. Simulation models can often be complex and slow which lead to the development of simulation meta-models that are simpler and faster models of complex simulation models. Artificial neural networks (ANNs) have been studied for use as simulation meta-models with different results. This final year project further studies the use of ANNs as simulation meta-models by comparing the predictability of five different neural network architectures: feed-forward-, generalized feed-forward-, modular-, radial basis- and Elman artificial neural networks where the underlying simulation is of complex production system. The results where that all architectures gave acceptable results even though it can be said that Elman- and feed-forward ANNs performed the best of the tests conducted here. The difference in accuracy and generalization was considerably small.

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Tanaka, Toshiyuki. "Control of growth dynamics of feed-forward neural network." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13445.

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Barth, Eric J. "Approximating discrete-time optimal control using a neural network." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/19009.

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Van, Zyl Jacobus. "Modelling chaotic systems with neural networks : application to seismic event predicting in gold mines." Thesis, Stellenbosch : University of Stellenbosch, 2001. http://hdl.handle.net/10019.1/4580.

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Thesis (MSc (Computer Science))-- University of Stellenbosch, 2001.
ENGLISH ABSTRACT: This thesis explores the use of neural networks for predicting difficult, real-world time series. We first establish and demonstrate methods for characterising, modelling and predicting well-known systems. The real-world system we explore is seismic event data obtained from a South African gold mine. We show that this data is chaotic. After preprocessing the raw data, we show that neural networks are able to predict seismic activity reasonably well.
AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die gebruik van neurale netwerke om komplekse, werklik bestaande tydreekse te voorspel. Ter aanvang noem en demonstreer ons metodes vir die karakterisering, modelering en voorspelling van bekende stelsels. Ons gaan dan voort en ondersoek seismiese gebeurlikheidsdata afkomstig van ’n Suid-Afrikaanse goudmyn. Ons wys dat die data chaoties van aard is. Nadat ons die rou data verwerk, wys ons dat neurale netwerke die tydreekse redelik goed kan voorspel.
Integrated Seismic Systems International

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Al-Hindi, Khalid A. "Flexible basis function neural networks for efficient analog implementations /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3074367.

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Myers, James William. "Stochastic algorithms for learning with incomplete data an application to Bayesian networks /." Full text available online (restricted access), 1999. http://images.lib.monash.edu.au/ts/theses/Myers.pdf.

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Liu, Ying Kin. "Load-distributing algorithm using fuzzy neural network and fault-tolerant framework /." access abstract and table of contents access full-text, 2006. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-ee-b21471423a.pdf.

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Thesis (M.Phil.)--City University of Hong Kong, 2006.
"Submitted to Department of Electronic Engineering in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 88-92)

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Wray, Barry A. "Prediction and control in a just-in-time environment using neural networks." Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-06062008-170827/.

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Brewer, Michael Robert. "Neural networks for meteorological satellite image interpretation." Thesis, University of Oxford, 1997. http://ora.ox.ac.uk/objects/uuid:55ee7430-4029-47de-adb7-4b611ba1edc6.

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Meteorological satellite images at visible and infra-red wavelengths are an invaluable source of information on cloud systems because of their extensive coverage of the whole of the Earth's surface, providing data in areas that are only sparsely monitored, if at all, by other means. Although this information has been used subjectively by forecasters for many years, the lack of automatic, quantitative analysis techniques largely prevents its assimilation into numerical weather prediction (NWP) models, which are the basis of all modern weather forecasting. This thesis investigates the use of neural network techniques for the analysis of the images in order to make fuller use of the available data. The recognition of a particular type of cloud is dependent on the determination of a set of features from the satellite image spectral bands that will give discriminating information. This feature extraction and selection process is dealt with in detail, and a feature selection process based on the radial basis function (RBF) neural network is presented. The high-dimensional feature space is visualized on a two-dimensional plane by the use of three techniques: the Kohonen map, the Sammon map, and a recently-developed technique known as the Generative Topographic Mapping (GTM). Classification results using a multi-layer perceptron (MLP) and an RBF neural network are presented. The results of independently classifying each pixel in an image are compared with a method that makes use of contextual information, the Markov Random Field (MRF) model. The limitations of the pixel-based approach are highlighted, and a region-based approach is presented that enables the definition of large-scale regions of uniform cloud type. Two segmentation methods are used, the active contour (or snake) model, and the more recentlydeveloped level set technique. The latter method was found to provide many benefits over the former. The region-based approach will facilitate the assimilation of cloud system information into NWP models in the future.

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Zuo, Wei. "Fourier neural network based tracking control for nonlinear systems /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?MECH%202008%20ZUO.

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Fernandes, John Manuel. "Wireless industrial intelligent controller for a non-linear system." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/9021.

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Modern neural network (NN) based control schemes have surmounted many of the limitations found in the traditional control approaches. Nevertheless, these modern control techniques have only recently been introduced for use on high-specification Programmable Logic Controllers (PLCs) and usually at a very high cost in terms of the required software and hardware. This ‗intelligent‘ control in the sector of industrial automation, specifically on standard PLCs thus remains an area of study that is open to further research and development. The research documented in this thesis examined the effectiveness of linear traditional control schemes such as Proportional Integral Derivative (PID), Lead and Lead-Lag control, in comparison to non-linear NN based control schemes when applied on a strongly non-linear platform. To this end, a mechatronic-type balancing system, namely, the Ball-on-Wheel (BOW) system was designed, constructed and modelled. Thereafter various traditional and intelligent controllers were implemented in order to control the system. The BOW platform may be taken to represent any single-input, single-output (SISO) non-linear system in use in the real world. The system makes use of current industrial technology including a standard PLC as the digital computational platform, a servo drive and wireless access for remote control. The results gathered from the research revealed that NN based control schemes (i.e. Pure NN and NN-PID), although comparatively slower in response, have greater advantages over traditional controllers in that they are able to adapt to external system changes as well as system non-linearity through a process of learning. These controllers also reduce the guess work that is usually involved with the traditional control approaches where cumbersome modelling, linearization or manual tuning is required. Furthermore, the research showed that online-learning adaptive traditional controllers such as the NN-PID controller which maintains the best of both the intelligent and traditional controllers may be implemented easily and with minimum expense on standard PLCs.

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Castellano, Pierre John. "Speaker recognition modelling with artificial neural networks." Thesis, Queensland University of Technology, 1997.

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勞偉籌 and Wai-chau Edward Lo. "Servo control of robotic manipulator with artificial neural network." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31235128.

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Wang, Ying, and 王鷹. "On-line fault diagnosis of nonlinear dynamical systems using recurrentneural networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31242388.

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Wang, Feng. "Neural network model of memory reinforcement for text-based intelligent tutoring system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0021/NQ30122.pdf.

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Abidogun, Olusola Adeniyi. "Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks." Thesis, University of the Western Cape, 2005. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention,
marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process.

This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns.

Our investigation shows the learning ability of both techniques to discriminate user call patterns
the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data
from a real mobile telecommunication network.

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Dissertations / Theses on the topic 'Stochastic systems; neural networks; computer science'

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