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Neural networks

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Articles 31 - 45 of 45

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Entity Resolution Using Convolutional Neural Network, Ram Deepak Gottapu, Cihan H. Dagli, Bharami Ali Nov 2016

Entity Resolution Using Convolutional Neural Network, Ram Deepak Gottapu, Cihan H. Dagli, Bharami Ali

Engineering Management and Systems Engineering Faculty Research & Creative Works

Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural …

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Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau Jun 2016

Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for …

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Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke Nov 2015

Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Volatility forecasting models are becoming more accurate, but noise looks to be an inseparable part of these forecasts. Nonetheless, using adaptive filters to cancel the noise should help improve the performance of the forecasting models. Adaptive filters have the advantage of changing based on the environment. This feature is vital when they are used along with a model for volatility forecasting and error cancellation in the financial markets. Nonlinear Autoregressive (NAR) neural networks have simple structures, but they are efficient tools in error cancelation systems when working with non-stationary and random walk noise processes. For this research, an adaptive threshold …

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High-Performance Extreme Learning Machines: A Complete Toolbox For Big Data Applications, Anton Akusok, Kaj Mikael Bjork, Yoan Miche, Amaury Lendasse Jan 2015

High-Performance Extreme Learning Machines: A Complete Toolbox For Big Data Applications, Anton Akusok, Kaj Mikael Bjork, Yoan Miche, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Complete Approach to a Successful Utilization of a High-Performance Extreme Learning Machines (Elms) Toolbox for Big Data. It Summarizes Recent Advantages in Algorithmic Performance; Gives a Fresh View on the Elm Solution in Relation to the Traditional Linear Algebraic Performance; and Reaps the Latest Software and Hardware Performance Achievements. the Results Are Applicable to a Wide Range of Machine Learning Problems and Thus Provide a Solid Ground for Tackling Numerous Big Data Challenges. the Included Toolbox is Targeted at Enabling the Full Potential of Elms to the Widest Range of Users.

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Computational Intelligence Based Complex Adaptive System-Of-Systems Architecture Evolution Strategy, Siddharth Agarwal Jan 2015

Computational Intelligence Based Complex Adaptive System-Of-Systems Architecture Evolution Strategy, Siddharth Agarwal

Doctoral Dissertations

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving …

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Nonlinear Modeling Using Neural Networks For Trading The Soybean Complex, Phoebe S. Wiles, David Lee Enke Jan 2014

Nonlinear Modeling Using Neural Networks For Trading The Soybean Complex, Phoebe S. Wiles, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Recently, there has been a spike in the prices and popularity of commodities. On a macroeconomic level, developing countries are increasing production; while on a microeconomic level, speculative traders are becoming more involved in the market. Agricultural products have a diverse array of factors that can affect the price (i.e. political, government, population, weather, supply and demand). Commodity prices can suffer from extreme volatility in the short term, changing as much as 50% in one year. This research uses the soybean crush spread as a model. The soybean complex adds an interesting component as the underlying soybean product can be …

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Modeling Small Unmanned Aerial System Mishaps Using Logistics Regression And Artificial Neural Networks, Sean E. Wolf Feb 2012

Modeling Small Unmanned Aerial System Mishaps Using Logistics Regression And Artificial Neural Networks, Sean E. Wolf

Theses and Dissertations

A dataset of 854 small unmanned aerial system (SUAS) flight experiments from 2005-2009 is analyzed to determine significant factors that contribute to mishaps. The data from 29 airframes of different designs and technology readiness levels were aggregated. 20 measured parameters from each flight experiment are investigated, including wind speed, pilot experience, number of prior flights, pilot currency, etc. Outcomes of failures (loss of flight data) and damage (injury to airframe) are classified by logistic regression modeling and artificial neural network analysis. From the analysis, it can be concluded that SUAS damage is a random event that cannot be predicted with …

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A Structure First Image Inpainting Approach Based On Self-Organizing Map (Som), Bo Chen, Zhaoxia Wang, Ming Bai, Quan Wang, Zhen Sun Dec 2010

A Structure First Image Inpainting Approach Based On Self-Organizing Map (Som), Bo Chen, Zhaoxia Wang, Ming Bai, Quan Wang, Zhen Sun

Research Collection School Of Computing and Information Systems

This paper presents a structure first image inpainting method based on self-organizing map (SOM). SOM is employed to find the useful structure information of the damaged image. The useful structure information which includes relevant edges of the image is used to simulate the structure information of the lost or damaged area in the image. The structure information is described by distinct or indistinct curves in an image in this paper. The obtained target curves separate the damaged area of the image into several parts. As soon as each part of the damaged image is restored respectively, the damaged image is …

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Multilayer Image Inpainting Approach Based On Neural Networks, Quan Wang, Zhaoxia Wang, Che Sau Chang, Ting Yang Aug 2009

Multilayer Image Inpainting Approach Based On Neural Networks, Quan Wang, Zhaoxia Wang, Che Sau Chang, Ting Yang

Research Collection School Of Computing and Information Systems

This paper describes an image inpainting approach based on the self-organizing map for dividing an image into several layers, assigning each damaged pixel to one layer, and then restoring these damaged pixels by the information of their respective layer. These inpainted layers are then fused together to provide the final inpainting results. This approach takes advantage of the neural network's ability of imitating human's brain to separate objects of an image into different layers for inpainting. The approach is promising as clearly demonstrated by the results in this paper.

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The Effect Of Model Formulation On The Comparative Performance Of Artificial Neural Networks And Regression, Michael F. Cochrane Apr 2002

The Effect Of Model Formulation On The Comparative Performance Of Artificial Neural Networks And Regression, Michael F. Cochrane

Engineering Management & Systems Engineering Theses & Dissertations

Multiple linear regression techniques have been traditionally used to construct predictive statistical models, relating one or more independent variables (inputs) to a dependent variable (output). Artificial neural networks can also be constructed and trained to learn these complex relationships, and have been shown to perform at least as well as linear regression on the same data sets. Research on the use of neural network models as alternatives to multivariate linear regression has focused predominantly on the effects of sample size, noise, and input vector size on the comparative performance of these two modeling techniques. However, research has also shown that …

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Feature Selection For Predicting Pilot Mental Workload, Julia A. East Mar 2000

Feature Selection For Predicting Pilot Mental Workload, Julia A. East

Theses and Dissertations

Advances in technology have the cockpits of the aircraft in the Air Force inventory increasingly complex. Consequently, mental demands on the pilot have risen. In some cases, mental demands were so overwhelming that pilots have forgotten basic flying techniques, such as G-straining maneuvers. The results have been fatal. Recent research in this area has involved collecting psychophysiological features, such as electroencephalography (EEG), heart, eye and respiration measures, in an attempt to identify pilot mental workload. This thesis focuses on feature selection and reduction of the psycophysiological features and subsequent classification of pilot mental workload on multiple subjects over multiple days. …

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Study Of Human Factors Variables In Battle Outcome Prediction Models, David Andrew Glovier Apr 1998

Study Of Human Factors Variables In Battle Outcome Prediction Models, David Andrew Glovier

Engineering Management & Systems Engineering Theses & Dissertations

Over time there have been many improvements in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research …

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A Parallel Genetic-Neuro Scheduler For Job-Shop Scheduling Problems, H. C. Lee, Cihan H. Dagli Aug 1997

A Parallel Genetic-Neuro Scheduler For Job-Shop Scheduling Problems, H. C. Lee, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Despite relentless efforts on developing new approaches, there are still large gaps between schedules generated through various planning systems, and schedules actually used in the shop floor environment. An effective schedule generation is a knowledge intensive activity requiring a comprehensive model of a factory and its environment at all times. There are four main difficulties that need to be addressed. First, job shop scheduling belongs to a class of NP-hard problems. Second, it is a highly constrained problem that changes from shop to shop. Third, scheduling decisions depend upon other decisions which are not isolated from other functions. Thus, it …

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Recurrent Neural Networks For Radar Target Identification, Eric T. Kouba Dec 1992

Recurrent Neural Networks For Radar Target Identification, Eric T. Kouba

Theses and Dissertations

A real-time recurrent learning algorithm was applied to a five class radar target identification problem. The wideband radar was assumed to measure both kinematic (tracking information expressed as estimated aspect angles) and high range resolution data from a single, isolated aircraft. The aspect angles (azimuth and elevation) of the aircraft relative to the radar were assumed to be constantly chancing. This created temporal sequences of high range resolution radar signatures that changed as the aspect angles changed. These sequences were used as input features to a recurrent neural network for three radar target identification test cases. The first test case …

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Multilayer Perceptrons For Classification, Lisa M. Belue Mar 1992

Multilayer Perceptrons For Classification, Lisa M. Belue

Theses and Dissertations

Techniques for training, testing, and validating multilayer perceptrons are thoroughly examined. Results obtained using perceptrons are compared and contrasted with two multivariate discriminant analysis techniques- logistic regression and k neighbor. Methods for determining significant input features are investigated and a procedure for examining the confidence to place in the significance of these features is developed. Procedures to evaluate the applicability of high-order feature inputs are examined. These methods and procedures are applied to two very different applications. The first application concerns the prediction of Air Force pilot retention/separation rates for input to force projection models. The second application concerns the …

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