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Dissertations / Theses on the topic 'Training systems in artificial neural networks'

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Published: 5 June 2025

Last updated: 16 July 2025

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1

Melcherson, Tim. "Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

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Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex region and how it is effected by climate change. Here, animals and vegetation are tightly coupled with their environment in a fragile ecosystem, and when the environment undergo rapid changes it risks damaging these ecosystems severely. Understanding what kind of data that has potential to be used in artificial intelligence, can be of importance as many research stations have data archives from decades of work in the Arctic. In this thesis, a YOLOv4 object d

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2

Manesco, Luis Fernando. "Modelagem de um processo fermentativo por rede Perceptron multicamadas com atraso de tempo." Universidade de São Paulo, 1996. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-22012018-103016/.

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A utilização de Redes Neurais Artificias para fins de identificação e controle de sistemas dinâmicos têm recebido atenção especial de muitos pesquisadores, principalmente no que se refere a sistemas não lineares. Neste trabalho é apresentado um estudo sobre a utilização de um tipo em particular de Rede Neural Artificial, uma Perceptron Multicamadas com Atraso de Tempo, na estimação de estados da etapa fermentativa do processo de Reichstein para produção de vitamina C. A aplicação de Redes Neurais Artificiais a este processo pode ser justificada pela existência de problemas associados à esta et

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3

De, las Casas Zolezzi Humberto Jose. "Model-Free Optimization of Trajectory and Impedance Parameters on Exercise Robots with Applications to Human Performance and Rehabilitation." Cleveland State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1625490714196286.

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4

Lundström, Christoffer, and Camilla Heiding. "Predictive Autoscaling of Systems using Artificial Neural Networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104714.

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Autoscalers handle the scaling of instances in a system automatically based on specified thresholds such as CPU utilization. Reactive autoscalers do not take the delay of initiating a new instance into account, which may lead to overutilization. By applying machine learning methodology to predict future loads and the desired number of instances, it is possible to preemptively initiate scaling such that new instances are available before demand occurs. Leveraging efficient scaling policies keeps the costs and energy consumption low while ensuring the availability of the system. In this thesis,

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5

Berry, Ian Michael. "Data classification using unsupervised artificial neural networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.

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6

Shi, Arthur Q. X. "Structural Damage Assessment Using Artificial Neural Networks and Artificial Immune Systems." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1499.

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Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immu

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7

Kogel, Wendy E. "Faster Training of Neural Networks for Recommender Systems." Digital WPI, 2002. https://digitalcommons.wpi.edu/etd-theses/607.

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In this project we investigate the use of artificial neural networks(ANNs) as the core prediction function of a recommender system. In the past, research concerned with recommender systems that use ANNs have mainly concentrated on using collaborative-based information. We look at the effects of adding content-based information and how altering the topology of the network itself affects the accuracy of the recommendations generated. In particular, we investigate a mixture of experts topology. We create two expert clusters in the hidden layer of the ANN, one for content-based data and another fo

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8

Rodríguez, Villegas Antoni. "Polyp segmentation using artificial neural networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-98001.

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Colorectal cancer is the second cause of cancer death in the world. Aiming to early detect and prevent this type of cancer, clinicians perform screenings through the colon searching for polyps (colorectal cancer precursor lesions).If found, these lesions are susceptible of being removed in order to further ana-lyze their malignancy degree. Automatic polyp segmentation is of primary impor-tance when it comes to computer-aided medical diagnosis using images obtained in colonoscopy screenings. These results allow for more precise medical diagnosis which can lead to earlier detection.This project

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9

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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10

Hsu, Kuo-Lin, Hoshin Vijai Gupta, and Soroosh Sorooshian. "A SUPERIOR TRAINING STRATEGY FOR THREE-LAYER FEEDFORWARD ARTIFICIAL NEURAL NETWORKS." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1996. http://hdl.handle.net/10150/614171.

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A new algorithm is proposed for the identification of three-layer feedforward artificial neural networks. The algorithm, entitled LLSSIM, partitions the weight space into two major groups: the input- hidden and hidden -output weights. The input- hidden weights are trained using a multi -start SIMPLEX algorithm and the hidden -output weights are identified using a conditional linear- least- square estimation approach. Architectural design is accomplished by progressive addition of nodes to the hidden layer. The LLSSIM approach provides globally superior weight estimates with fewer functio

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11

George, Abhinav Kurian. "Fault tolerance and re-training analysis on neural networks." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552391639148868.

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12

Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbol

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13

Mustafa, Ghulam. "A methodology for contextual recommendation using artificial neural networks." Thesis, University of Bedfordshire, 2018. http://hdl.handle.net/10547/622833.

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Recommender systems are an advanced form of software applications, more specifically decision-support systems, that efficiently assist the users in finding items of their interest. Recommender systems have been applied to many domains from music to e-commerce, movies to software services delivery and tourism to news by exploiting available information to predict and provide recommendations to end user. The suggestions generated by recommender systems tend to narrow down the list of items which a user may overlook due to the huge variety of similar items or users’ lack of experience in the part

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14

Olmucci, Poddubnyy Oleksandr. "Graph Neural Networks for Recommender Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25033/.

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In recent years, a new type of deep learning models, Graph Neural Networks (GNNs), have demonstrated to be a powerful learning paradigm when applied to problems that can be described via graph data, due to their natural ability to integrate representations across nodes that are connected via some topological structure. One of such domains is Recommendation Systems, the majority of whose data can be naturally represented via graphs. For example, typical item recommendation datasets can be represented via user-item bipartite graphs, social recommendation datasets by social networks, and so on. T

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15

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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16

Sneath, Evan B. "Artificial neural network training for semi-autonomous robotic surgery applications." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416231638.

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17

Lundin, Johan. "Prediction of Protein Mutations Using Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-400.

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<p>This thesis is concerned with the prediction of protein mutations using artificial neural networks. From the biological perspective it is of interest to investigate weather it is possible to find rules of mutation between evolutionary adjacent (or closely related) proteins. Techniques from computer science are used in order to see if it is possible to predict protein mutations i.e. using artificial neural networks. The computer science perspective of this work would be to try optimizing the results from the neural networks. However, the focus of this thesis is primarily on the biological pe

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18

Chen, Lihui. "Modelling continuous sequential behaviour to enhance training and generalization in neural networks." Thesis, University of St Andrews, 1993. http://hdl.handle.net/10023/13485.

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This thesis is a conceptual and empirical approach to embody modelling of continuous sequential behaviour in neural learning. The aim is to enhance the feasibility of training and capacity for generalisation. By examining the sequential aspects of the passing of time in a neural network, it is suggested that an alteration to the usual goal weight condition may be made to model these aspects. The notion of a goal weight path is introduced, with a path-based backpropagation (PBP) framework being proposed. Two models using PBP have been investigated in the thesis. One is called Feedforward Contin

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19

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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20

Amarasinghe, Kasun. "Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6091.

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Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential. The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical System

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21

Ji, Wei. "Artificial neural networks and fuzzy systems in bladder cancer prognosis." Thesis, Coventry University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417616.

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22

Zivanovic, Rastko. "Artificial neural networks for state estimation of electric power systems." Doctoral thesis, University of Cape Town, 1996. http://hdl.handle.net/11427/9470.

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Includes bibliographical references.<br>This thesis deals with the application of Artificial Neural Network (ANN) technology in power system state estimation. It addresses the following developments: the fundamentals of the state estimation based on ANN technology; the feasible ANN state estimation methods; use of voltage phasor angle measurements in ANN state estimation; and bad data processing for ANN state estimation. The power system state estimation problem is formulated as an optimization problem applied to dynamic ANN model. Two types of dynamic ANN models are used: ANN model with steep

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23

Hsu, Cheng-Yu. "Condition monitoring of fluid power systems using artificial neural networks." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295443.

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24

Winter, Marylin. "Artificial neural networks and map-matching for GPS navigation." Thesis, University of South Wales, 2006. https://pure.southwales.ac.uk/en/studentthesis/artificial-neural-networks-and-mapmatching-for-gps-navigation(67bf2a4a-fcd2-4a86-8895-0ed296461bc5).html.

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Global navigation satellite systems (GNSS), such as the Global Positioning System (GPS) have been increasingly used in navigation and tracking of vehicles. Using GPS, certain positioning errors and limitations, such as multipath effects and the geometric position of the satellites (DOP) or signal obstructions by high buildings, trees and terrain, have to be considered. Generally travel on road or footpath, map-matching algorithms can be used to correlate the computed system location with a digital map network. Map Matched GPS (MMGPS) is a test-bed simulator for researching algorithms and techn

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25

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

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26

Rimer, Michael Edwin. "Improving Neural Network Classification Training." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2094.pdf.

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27

Jönsson, Jonatan, and Felix Stenbäck. "Fence surveillance with convolutional neural networks." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-37116.

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Broken fences is a big security risk for any facility or area with strict security standards. In this report we suggest a machine learning approach to automate the surveillance for chain-linked fences. The main challenge is to classify broken and non-broken fences with the help of a convolution neural network. Gathering data for this task is done by hand and the dataset is about 127 videos at 26 minutes length total on 23 diﬀerent locations. The model and dataset are tested on three performances traits, scaling, augmentation improvement and false rate. In these tests we concluded that nearest

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28

McMinn, David. "Using evolutionary artificial neural networks to design hierarchical animat nervous systems." Thesis, Robert Gordon University, 2001. http://hdl.handle.net/10059/427.

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The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to performing jobs of a single type. For these reasons, a better solution is desired. The system studied here is an Artificial Nervous System (ANS) which is biologically inspired; it is arranged as a hierarchy of layers containing modules operating in parallel. The ANS model has been developed to be flexible, scalable, extensible and modular. The ANS can be implemented using any su

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29

Steinsen, Ragnar Mar. "Predicting Transient Overloads in Real-Time Systems using Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-392.

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<p>The emerging need for dynamically scheduled real-time systems requires methods for handling transient overloads. Current methods have in common that they deal with transient overloads as they occur, which gives the real-time system limited time to react to the overload. In this work we enable new approaches to overload management. Our work shows that artificial neural networks (ANNs) can predict future transient overloads. This way the real-time system can prepare for a transient overload before it actually occurs. Even though the artificial neural network is not yet integrated into any sys

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30

Ponca, Marek Scarbata Gerd. "Towards efficient implementation of artificial neural networks in systems on chip /." Ilmenau : ISLE, 2007. http://www.gbv.de/dms/ilmenau/toc/530583380.PDF.

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31

Thompson, Ian M. "Artificial neural networks in medicine : theory and application in biomedical systems." Thesis, University of Newcastle Upon Tyne, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262994.

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32

Sêcco, Ney Rafael. "Training artificial neural networks to predict aerodynamic coefficients of airliner wing-fuselage configurations." Instituto Tecnológico de Aeronáutica, 2014. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=2955.

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Multi-disciplinary Design Optimization highly demands computational resources, therefore it is important to develop design tools with low computational cost without compromising the fidelity of the model. The main goal of this work was to establish a methodology of training artificial neural networks for specific purposes of aircraft aerodynamic design, in order to substitute a computational fluid dynamics software in an optimization framework. This neural network would predict the lift and drag coefficients for an airliner';s wing-fuselage configuration based on its planform, airfoil, and fli

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33

Hofer, Daniel G. Sbarbaro. "Connectionist feedforward networks for control of nonlinear systems." Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390248.

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34

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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35

Dionisi, Steven M. "Real Time Data Reduction and Analysis Using Artificial Neural Networks." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/611856.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>An artificial neural network (ANN) for use in real time data reduction and analysis will be presented. The use and advantage of hardware and software implementations of neural networks will be considered. The ability of neural networks to learn and store associations between different sets of data can be used to create custom algorithms for some of the data analysis done during missions. Once trained, the ANN can distill the signals from several sensors into a sin

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36

Castorina, Giovanni. "Artificial intelligence based hybrid systems for financial forecasting." Thesis, University of the West of England, Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365146.

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Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision

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37

Yildiz, Ali. "Resource-aware Load Balancing System With Artificial Neural Networks." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607613/index.pdf.

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As the distributed systems becomes popular, efficient load balancing systems taking better decisions must be designed. The most important reasons that necessitate load balancing in a distributed system are the heterogeneous hosts having different com- puting powers, external loads and the tasks running on different hosts but communi- cating with each other. In this thesis, a load balancing approach, called RALBANN, developed using graph partitioning and artificial neural networks (ANNs) is de- scribed. The aim of RALBANN is to integrate the successful load balancing

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38

Helgason, Magnus Thor. "Using Artificial Neural Networks for Admission Control in Firm Real-Time Systems." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-412.

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<p>Admission controllers in dynamic real-time systems perform traditional schedulability tests in order to determine whether incoming tasks will meet their deadlines. These tests are computationally expensive and typically run in n * log n time where n is the number of tasks in the system. An incoming task might therefore miss its deadline while the schedulability test is being performed, when there is a heavy load on the system. In our work we evaluate a new approach for admission control in firm real-time systems. Our work shows that ANNs can be used to perform a schedulability test in order

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Farooq, Assad. "Development of Prediction Systems Using Artificial Neural Networks for Intelligent Spinning Machines." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-37801.

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The optimization of the spinning process and adjustment of the machine settings involve “Trial and Error” method resulting in the wasting of production time and material. This situation becomes worse in the spinning mills where the speed and material changes are frequent. This research includes the use of artificial neural networks to provide the thinking ability to the spinning machines to improve the yarn spinning process. Draw frame, being the central part of the spinning preparation chain and last machine to rectify the variations in the fed slivers is the main focus of the research work.

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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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Gebben, Florian. "Modeling and Simulation of Solar Energy Harvesting Systems with Artificial Neural Networks." Thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29626.

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Simulations are a good method for the verification of the correct operation of solar-powered sensor nodes over the desired lifetime. They do, however, require accurate models to capture the influences of the loads and solar energy harvesting system. Artificial neural networks promise a simplification and acceleration of the modeling process in comparison to state-of-the-art modeling methods. This work focuses on the influence of the modeling process's different configurations on the accuracy of the model. It was found that certain parameters, such as the network's number of neurons and layers,

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Payne, Russell. "The application of artificial neural networks to combustion and heat exchanger systems." Thesis, University of South Wales, 2005. https://pure.southwales.ac.uk/en/studentthesis/the-application-of-artificial-neural-networks-to-combustion-and-heat-exchanger-systems(684a7758-1b1c-4560-8df1-e482b42ef8a2).html.

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The operation of large industrial scale combustion systems, such as furnaces and boilers is increasingly dictated by emission legislation and requirements for improved efficiency. However, it can be exceedingly difficult and time consuming to gather the information required to improve original designs. Mathematical modelling techniques have led to the development of sophisticated furnace representations that are capable of representing combustion parameters. Whilst such data is ideal for design purposes, the current power of computing systems tends to generate simulation times that are too gre

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Shadabi, Fariba, and N/A. "Medical Outcome Prediction: A Hybrid Artificial Neural Networks Approach." University of Canberra. Information Sciences & Engineering, 2007. http://erl.canberra.edu.au./public/adt-AUC20070816.130444.

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This thesis advances the understanding of the application of artificial neural networks ensemble to clinical data by addressing the following fundamental question: What is the potentiality of an ensemble of neural networks models as a filter and classifier in a complex clinical situation? A novel neural networks ensemble classification model called Rules and Information Driven by Consistency in Artificial Neural Networks Ensemble (RIDCANNE) is developed for the purpose of prediction of medical outcomes or events, such as kidney transplants. The proposed classification model is based on combina

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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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<p>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-mod

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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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Khadapkar, Shailesh Sunil. "Failure identification of gear systems using Hilbert-Huang transform and artificial neural networks." Diss., Online access via UMI:, 2006.

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47

Alistair, Chalk. "PREDICTION OF PROTEIN SECONDARY STRUCTURE by Incorporating Biophysical Information into Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-235.

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<p>This project applied artificial neural networks to the field of secondary structure prediction of proteins. A NETtalk architecture with a window size 13 was used. Over-fitting was avoided by the use of 3 real numbers to represent amino acids, reducing the number of adjustable weights to 840. Two alternative representations of amino acids that incorporated biophysical data were created and tested. They were tested both separately and in combination on a standard 7-fold cross-validation set of 126 proteins. The best performance was achieved using an average result from two predictions. This w

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48

Geisen, Stefan [Verfasser], Ekkehard [Akademischer Betreuer] Sachs, and Volker [Akademischer Betreuer] Schulz. "Robust Training of Artificial Neural Networks via p-Quasinorms / Stefan Geisen ; Ekkehard Sachs, Volker Schulz." Trier : Universität Trier, 2020. http://d-nb.info/1215904975/34.

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Ahmed, Shamsuddin. "Development of self-adaptive back propagation and derivative free training algorithms in artificial neural networks." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2000. https://ro.ecu.edu.au/theses/1539.

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Three new iterative, dynamically self-adaptive, derivative-free and training parameter free artificial neural network (ANN) training algorithms are developed. They are defined as self-adaptive back propagation, multi-directional and restart ANN training algorithms. The descent direction in self-adaptive back propagation training is determined implicitly by a central difference approximation scheme, which chooses its step size according to the convergence behavior of the error function. This approach trains an ANN when the gradient information of the corresponding error function is not readily

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Jaquet, Jean-Michel. "A non-linear approach to modelling motivation in the workplace using artificial neural networks." Doctoral thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/13956.

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Includes bibliographical references.<br>The standard business conception of the employee is as a blank slate machine motivated through a behaviourist system of reward and punishment. In contrast to this conception, studies of human evolution, neurology and cognition suggest that motivation emerges from the interaction of a complex and non-linear system of variables. This two-part study uses a conceptual model of work motivation based on systems and complexity theory to identify and interpret the significance of outlying variables in the motivations of groups of working professionals with diffe

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