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Neural networks (Computer science)

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Articles 31 - 60 of 86

Full-Text Articles in Engineering

Computational Capabilities Of Leaky Integrate-And-Fire Neural Networks For Liquid State Machines, Amin Almassian, Christof Teuscher May 2013

Computational Capabilities Of Leaky Integrate-And-Fire Neural Networks For Liquid State Machines, Amin Almassian, Christof Teuscher

Student Research Symposium

We analyze the computational capability of Leaky Integrate-and-Fire (LIF) Neural Networks used as a reservoir (liquid) in the framework of Liquid State Machines (LSM). Maass et. al. investigated LIF neurons in LSM and their results showed that they are capable of noise-robust, parallel, and real-time computation. However, it still remains an open question how the network topology affects the computational capability of a reservoir. To address that question, we investigate the performance of the reservoir as a function of the average reservoir connectivity. We also show that the dynamics of the LIF reservoir is sensitive to changes in the average …

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Evolution Through The Search For Novelty, Joel Lehman Jan 2012

Evolution Through The Search For Novelty, Joel Lehman

Electronic Theses and Dissertations

I present a new approach to evolutionary search called novelty search, wherein only behavioral novelty is rewarded, thereby abstracting evolution as a search for novel forms. This new approach contrasts with the traditional approach of rewarding progress towards the objective through an objective function. Although they are designed to light a path to the objective, objective functions can instead deceive search into converging to dead ends called local optima. As a significant problem in evolutionary computation, deception has inspired many techniques designed to mitigate it. However, nearly all such methods are still ultimately susceptible to deceptive local optima because they …

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Intelligent Controls For A Semi-Active Hydraulic Prosthetic Knee, Timothy Allen Wilmot Jan 2011

Intelligent Controls For A Semi-Active Hydraulic Prosthetic Knee, Timothy Allen Wilmot

ETD Archive

We discuss open loop control development and simulation results for a semi-active above-knee prosthesis. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. We develop open loop control using biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm, and gradient descent. We use gradient descent to show that the control generated by BBO is locally optimal. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to complex, real-world, nonlinear, time varying control problems. The research contributes …

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Design And Operation Of Stationary Distributed Battery Micro-Storage Systems, Ala R. Al-Haj Hussein Jan 2011

Design And Operation Of Stationary Distributed Battery Micro-Storage Systems, Ala R. Al-Haj Hussein

Electronic Theses and Dissertations

Due to some technical and environmental constraints, expanding the current electric power generation and transmission system is being challenged by even increasing the deployment of distributed renewable generation and storage systems. Energy storage can be used to store energy from utility during low-demand (off-peak) hours and deliver this energy back to the utility during high-demand (on-peak) hours. Furthermore, energy storage can be used with renewable sources to overcome some of their limitations such as their strong dependence on the weather conditions, which cannot be perfectly predicted, and their unmatched or out-of-synchronization generation peaks with the demand peaks. Generally, energy storage …

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Effective Task Transfer Through Indirect Encoding, Phillip Verbancsics Jan 2011

Effective Task Transfer Through Indirect Encoding, Phillip Verbancsics

Electronic Theses and Dissertations

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea …

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Using Oceanic-Atmospheric Oscillations For Long Lead Time Streamflow Forecasting, Ajay Kalra, Sajjad Ahmad Mar 2009

Using Oceanic-Atmospheric Oscillations For Long Lead Time Streamflow Forecasting, Ajay Kalra, Sajjad Ahmad

Civil and Environmental Engineering and Construction Faculty Research

We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the …

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Combat Identification Modeling Using Neural Network Techniques, Changwook Lim Mar 2009

Combat Identification Modeling Using Neural Network Techniques, Changwook Lim

Theses and Dissertations

The purposes of this research were: (1) validating Kim’s (2007) simulation method by applying analytic methods and (2) comparing the two different Robust Parameter Design methods with three measures of performance (label accuracy for enemy, friendly, and clutter). Considering the features of CID, input variables were defined as two controllable (threshold combination of detector and classifier) and three uncontrollable (map size, number of enemies and friendly). The first set of experiments considers Kim’s method using analytical methods. In order to create response variables, Kim’s method uses Monte Carlo simulation. The output results showed no difference between simulation and the analytic …

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Metamodeling Techniques To Aid In The Aggregation Process Of Large Hierarchical Simulation Models, June F.D. Rodriguez Aug 2008

Metamodeling Techniques To Aid In The Aggregation Process Of Large Hierarchical Simulation Models, June F.D. Rodriguez

Theses and Dissertations

This research investigates how aggregation is currently conducted for simulation of large systems. The purpose is to examine how to achieve suitable aggregation in the simulation of large systems. More specifically, investigating how to accurately aggregate hierarchical lower-level (higher resolution) models into the next higher-level in order to reduce the complexity of the overall simulation model. The focus is on the exploration of the different aggregation techniques for hierarchical lower-level (higher resolution) models into the next higher-level. We develop aggregation procedures between two simulation levels (e.g., aggregation of engagement level models into a mission level model) to address how much …

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Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank Oct 2007

Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank

Electrical and Computer Engineering Faculty Research & Creative Works

Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene …

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Approximate Dynamic Programming And Neural Networks On Game Hardware, Ryan J. Meuth, Donald C. Wunsch Aug 2007

Approximate Dynamic Programming And Neural Networks On Game Hardware, Ryan J. Meuth, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Modern graphics processing units (GPU) and game consoles are used for much more than simply 3D graphics applications and video games. From machine vision to finite element analysis, GPU's are being used in diverse applications, collectively called General Purpose computation onf graphics processor units (GPGPU). Additionally, game consoles are entering the market of high performance computing as inexpensive nodes in computing clusters. This paper explores the capabilities and limitations of modern GPU's and game consoles, surveying the ADP and neural network technologies that can be applied to these devices.

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Combined Training Of Recurrent Neural Networks With Particle Swarm Optimization And Backpropagation Algorithms For Impedance Identification, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine Apr 2007

Combined Training Of Recurrent Neural Networks With Particle Swarm Optimization And Backpropagation Algorithms For Impedance Identification, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine

Electrical and Computer Engineering Faculty Research & Creative Works

A recurrent neural network (RNN) trained with a combination of particle swarm optimization (PSO) and backpropagation (BP) algorithms is proposed in this paper. The network is used as a dynamic system modeling tool to identify the frequency-dependent impedances of power electronic systems such as rectifiers, inverters, and DC-DC converters. As a category of supervised learning methods, the various backpropagation training algorithms developed for recurrent neural networks use gradient descent information to guide their search for optimal weights solutions that minimize the output errors. While they prove to be very robust and effective in training many types of network structures, they …

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Neural Network Based Method For Predicting Nonlinear Load Harmonics, Joy Mazumdar, Ronald G. Harley, Frank C. Lambert, Ganesh K. Venayagamoorthy Jan 2007

Neural Network Based Method For Predicting Nonlinear Load Harmonics, Joy Mazumdar, Ronald G. Harley, Frank C. Lambert, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Generation of harmonics and the existence of waveform pollution in power system networks are important problems facing the power utilities. The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system in recent years. Interaction between loads and sources in a power distribution network is a complex process and often not possible to explain analytically without making assumptions. The determination of true harmonic current distortion of a load is further complicated by the fact that the supply voltage waveform at the point of common coupling (PCC) is rarely a pure …

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Optimal Wide Area Controller And State Predictor For A Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 2007

Optimal Wide Area Controller And State Predictor For A Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

An optimal wide area controller is designed in this paper for a 12-bus power system together with a Static Compensator (STATCOM). The controller provides auxiliary reference signals for the automatic voltage regulators (AVR) of the generators as well as the line voltage controller of the STATCOM in such a way that it improves the damping of the rotor speed deviations of the synchronous machines. Adaptive critic designs theory is used to implement the controller and enable it to provide nonlinear optimal control over the infinite horizon time of the problem and at different operating conditions of the power system. Simulation …

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Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow Jan 2007

Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow

Engineering Management and Systems Engineering Faculty Research & Creative Works

Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly …

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A Comparison Of Main Rotor Smoothing Adjustments Using Linear And Neural Network Algorithms, Nathan A. Miller Mar 2006

A Comparison Of Main Rotor Smoothing Adjustments Using Linear And Neural Network Algorithms, Nathan A. Miller

Theses and Dissertations

Helicopter main rotor smoothing is a maintenance procedure that is routinely performed to minimize airframe vibrations induced by non-uniform mass and/or aerodynamic distributions in the main rotor system. This important task is both time consuming and expensive, so improvements to the process have long been sought. Traditionally, vibrations have been minimized by calculating adjustments based on an assumed linear relationship between adjustments and vibration response. In recent years, artificial neural networks have been trained to recognize non-linear mappings between adjustments and vibration response. This research was conducted in order observe the character of the adjustment mapping of the Vibration Management …

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Survey Of Clustering Algorithms, Rui Xu, Donald C. Wunsch May 2005

Survey Of Clustering Algorithms, Rui Xu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani Feb 2005

Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel neural network (NN) -based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NN: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. The magnitude constraints are manifested as saturation nonlinearities in the output feedback …

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An Investigation Of The Effects Of Correlation, Autocorrelation, And Sample Size In Classifier Fusion, Nathan J. Leap Mar 2004

An Investigation Of The Effects Of Correlation, Autocorrelation, And Sample Size In Classifier Fusion, Nathan J. Leap

Theses and Dissertations

This thesis extends the research found in Storm, Bauer, and Oxley, 2003. Data correlation effects and sample size effects on three classifier fusion techniques and one data fusion technique were investigated. Identification System Operating Characteristic Fusion (Haspert, 2000), the Receiver Operating Characteristic Within Fusion method (Oxley and Bauer, 2002), and a Probabilistic Neural Network were the three classifier fusion techniques; a Generalized Regression Neural Network was the data fusion technique. Correlation was injected into the data set both within a feature set (autocorrelation) and across feature sets for a variety of classification problems, and sample size was varied throughout. Total …

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Blackfingers A Sophisticated Hand Prothesis, Michele Folgheraiter, Giuseppina Gini, Marek Perkowski, Mikhail Pivtoraiko Apr 2003

Blackfingers A Sophisticated Hand Prothesis, Michele Folgheraiter, Giuseppina Gini, Marek Perkowski, Mikhail Pivtoraiko

Electrical and Computer Engineering Faculty Publications and Presentations

Our goal is to develop the low level control system for an artificial hand ”Blackfingers”. Blackfingers was thought to have two main applications: as humanoid robot’s hand or as a human hand prothesis. In this last application our intent is to realize a device more sophisticated respect the actual commerce prothesis. Also in our intention is to use some biological paradigms to create a human like reflex control easy to be interfaced also with the human nervous system. In this paper we illustrate the properties and the morphology of a human like neural reflex controller, used to set the stiffness …

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An Investigation Of The Effects Of Correlation In Sensor Fusion, Susan A. Storm Mar 2003

An Investigation Of The Effects Of Correlation In Sensor Fusion, Susan A. Storm

Theses and Dissertations

This thesis takes the first step towards the creation of a synthetic classifier fusion-testing environment. The effects of data correlation on three classifier fusion techniques were examined. The three fusion methods tested were the ISOC fusion method (Haspert, 2000), the ROC "Within" Fusion method (Oxley and Bauer, 2002) and the simple use of a Probabilistic Neural Network (PNN) as a fusion tool. Test situations were developed to allow the examination of various levels of correlation both between and within feature streams. The effects of training a fusion ensemble on a common dataset versus an independent data set were also contrasted. …

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Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar Jun 2002

Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar

Electrical & Computer Engineering Faculty Research

The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors experiments, in some cases, argument reduction, makes the learning process harder.

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Le Bio Wall : Un Tissue Informatique Pour Le Prototypage De Systèmes Bio-Inspirés, Andre Stauffer, Daniel Mange, Gianluca Tempesti, Christof Teuscher Apr 2002

Le Bio Wall : Un Tissue Informatique Pour Le Prototypage De Systèmes Bio-Inspirés, Andre Stauffer, Daniel Mange, Gianluca Tempesti, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

Dans cet article, nous décrivons le BioWall, un tissu informatique reconfigurable géant développé dans le but d’y implémenter des machines mettant en oeuvre les principes de notre projet Embryonique. Bien que ses dimensions et ses caractéristiques en font d’abord un objet de démonstration publique, le BioWall constitue également un outil de recherche précieux, du fait que sa faculté de reprogrammation et sa structure cellulaire s’adaptent parfaitement à l’implémentation de toutes sortes de systèmes bioinspirés. Pour illustrer ces capacités, nous décrivons un ensemble d’applications qui reflètent différentes sources d’inspiration biologique allant des systèmes biologiques ontogénétiques aux dispositifs évolutifs phylogénétiques, en passant …

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An Integrated Architecture And Feature Selection Algorithm For Radial Basis Neural Networks, Timothy D. Flietstra Mar 2002

An Integrated Architecture And Feature Selection Algorithm For Radial Basis Neural Networks, Timothy D. Flietstra

Theses and Dissertations

There are two basic ways to control an Unmanned Combat Aerial Vehicle (UCAV) as it searches for targets: allow the UCAV to act autonomously or employ man-in-the-loop control. There are also two target sets of interest: fixed or mobile targets. This research focuses on UCAV-based targeting of mobile targets using man-in-the-loop control. In particular, the interest is in how levels of satellite signal latency or signal degradation affect the ability to accurately track, target, and attack mobile targets. This research establishes a weapon effectiveness model assessing targeting inaccuracies as a function of signal latency and/or signal degradation. The research involved …

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Voice Command Controller, Hoang Nghia Nguyen Jan 2000

Voice Command Controller, Hoang Nghia Nguyen

Theses : Honours

Signal processing technology has been strongly developed and it has attracted interest from scientists and engineers around the world from the last decade. Speech synthesis and speech recognition are particular topic in the field that have been widely used and developed in many different area such as business, controlling, education and entertainment. The project's main objective is to study and develop an application program with the Speech SDK through design and implementation of Tele-Control system based on the commercial product of National Semiconductor: Carrier-Current Transceiver (LM 1893) and Speech development kit (Speech SDK4.0) from Microsoft Corporation. The project is suitable …

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Feature Saliency In Artificial Neural Networks With Application To Modeling Workload, Kelly A. Greene Dec 1998

Feature Saliency In Artificial Neural Networks With Application To Modeling Workload, Kelly A. Greene

Theses and Dissertations

This dissertation research extends the current knowledge of feature saliency in artificial neural networks (ANN). Feature saliency measures allow for the user to rank order the features based upon the saliency, or relative importance, of the features. Selecting a parsimonious set of salient input features is crucial to the success of any ANN model. In this research, several methodologies were developed using the Signal to Noise Ratio (SNR) Feature Screening Method and its associated SNR Saliency Measure for selecting a parsimonious set of salient features to classify pilot workload in addition to air traffic controller workload. Candidate features were derived …

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Training Strategies For Critic And Action Neural Networks In Dual Heuristic Programming Method, Christian Peter Paintz May 1997

Training Strategies For Critic And Action Neural Networks In Dual Heuristic Programming Method, Christian Peter Paintz

Dissertations and Theses

This thesis discusses strategies for and details of training procedures for the Dual Heuristic Programming (DHP) methodology. This and other approximate dynamic programming approaches (HDP, DHP, GDHP) have been discussed in some detail in the literature, all being members of the Adaptive Critic Design (ACD) family. The example applications used here are the inverted pendulum problem and a fully nonlinear constant velocity bicycle steering model. The inverted pendulum has been successfully controlled using DHP, as reported in the literature. This thesis suggests and investigates several alternative D HP training procedures and compares their performance with respect to convergence speed and …

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Experiments In Aggregating Air Ordnance Effectiveness Data For The Tacwar Model, James E. Parker Feb 1997

Experiments In Aggregating Air Ordnance Effectiveness Data For The Tacwar Model, James E. Parker

Theses and Dissertations

An interactive MS Access&trademark; based application that aggregates the output of the SABSEL model for input into the TACWAR model is developed. The application was developed following efforts to create a functional approximation of the SABSEL data using neural networks, statistical networks, and traditional statistical techniques. These approximations were compared to a look-up table methodology on the basis of accuracy, (RMSE

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Pulse Coupled Neural Networks For The Segmentation Of Magnetic Resonance Brain Images, Shane L. Abrahamson Dec 1996

Pulse Coupled Neural Networks For The Segmentation Of Magnetic Resonance Brain Images, Shane L. Abrahamson

Theses and Dissertations

This research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image segmentation has proven difficult, primarily due to scanning artifacts such as interscan and intrascan intensity inhomogeneities. The method developed and presented here uses a PCNN to both filter and segment MR brain images. The technique begins by preprocessing images with a PCNN filter to reduce scanning artifacts. Images are then contrast enhanced via histogram equalization. Finally, a PCNN is used to segment the images to arrive at the final result. Modifications to the original PCNN model are …

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Adaptive Neural Network Controller For Atm Traffic, Jeffrey E. Larson Dec 1996

Adaptive Neural Network Controller For Atm Traffic, Jeffrey E. Larson

Theses and Dissertations

Broadband-Integrated Services Digital Networks (B-ISDN), along with Asynchronous Transfer Mode (ATM), were designed to meet the requirements of modern communication networks to handle multiple users and a wide variety of diverse traffic including voice, data and video. ATM responds to requests for admission to the network by analyzing whether or not the grade of service (GOS) requirement, specified in the admission request, can be guaranteed without violating the GOS guaranteed to traffic already accepted into the network. The GOS is typically a parameter such as cell loss rate (CLR), average delay, or some other measurement associated with network performance. In …

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An Investigation Of Preliminary Feature Screening Using Signal-To-Noise Ratios, David B. Sumrell Mar 1996

An Investigation Of Preliminary Feature Screening Using Signal-To-Noise Ratios, David B. Sumrell

Theses and Dissertations

A new saliency metric and a new saliency screening method are developed. This new metric, the SN saliency metric, is based upon signal-to-noise ratios, where the signal is provided by a sum of squared weights associated with a given feature, and the noise is based upon a sum of squared weights associated with a reference noise feature which is injected into the data. The resultant metric allows for a direct comparison of the feature of interest with a reference noise feature which is known to be nonsalient. The SN saliency screening method, which uses the SN saliency metric, offers the …

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