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Artificial intelligence

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Articles 91 - 120 of 120

Full-Text Articles in Electrical and Computer Engineering

Cyber-Physical Embedded Systems With Transient Supervisory Command And Control: A Framework For Validating Safety Response In Automated Collision Avoidance Systems, Daniel K. Trembley Jan 2018

Cyber-Physical Embedded Systems With Transient Supervisory Command And Control: A Framework For Validating Safety Response In Automated Collision Avoidance Systems, Daniel K. Trembley

Graduate Dissertations and Theses

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness …

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Fundamentals Of Neutrosophic Logic And Sets And Their Role In Artificial Intelligence (Fundamentos De La Lógica Y Los Conjuntos Neutrosóficos Y Su Papel En La Inteligencia Artificial ), Florentin Smarandache, Maykel Leyva-Vazquez Jan 2018

Fundamentals Of Neutrosophic Logic And Sets And Their Role In Artificial Intelligence (Fundamentos De La Lógica Y Los Conjuntos Neutrosóficos Y Su Papel En La Inteligencia Artificial ), Florentin Smarandache, Maykel Leyva-Vazquez

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophy is a new branch of philosophy which studies the origin, nature and scope of neutralities. This has formed the basis for a series of mathematical theories that generalize the classical and fuzzy theories such as the neutrosophic sets and the neutrosophic logic. In the paper, the fundamental concepts related to neutrosophy and its antecedents are presented. Additionally, fundamental concepts of artificial intelligence will be defined and how neutrosophy has come to strengthen this discipline.

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Convolutional Neural Network Based Localized Classification Of Uterine Cervical Cancer Digital Histology Images, Haidar A. Almubarak, R. Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier Oct 2017

Convolutional Neural Network Based Localized Classification Of Uterine Cervical Cancer Digital Histology Images, Haidar A. Almubarak, R. Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier

Electrical and Computer Engineering Faculty Research & Creative Works

In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. Image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to …

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Pure Fuzzy Hall Effect Sensors For Permanent Magnet Synchronous Motor, İbrahi̇m Alişkan, Rüstem Yilmazel Jan 2016

Pure Fuzzy Hall Effect Sensors For Permanent Magnet Synchronous Motor, İbrahi̇m Alişkan, Rüstem Yilmazel

Turkish Journal of Electrical Engineering and Computer Sciences

An investigation about Hall effect sensors' efficiency is confirmed in permanent magnet synchronous motor (PMSM) drive systems. A fuzzy control algorithm is used as an artificial intelligence controller. Large scale and low slopes are used for creating membership functions and a sensitive controller is obtained. Speed is wanted to be taken under control and a minimum error value is aimed. PMSM drive systems are established using MATLAB-Simulink/SimPower. Simulations are realized with real-time parameters in discrete mode. A fuzzy logic controller is designed by using the MATLAB/Fuzzy Logic Toolbox. A normalization technique and high resolution output of the fuzzy logic controller …

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Internet Of Things To Smart Iot Through Semantic, Cognitive, And Perceptual Computing, Amit P. Sheth Jan 2016

Internet Of Things To Smart Iot Through Semantic, Cognitive, And Perceptual Computing, Amit P. Sheth

Publications

Rapid growth in the Internet of Things (IoT) has resulted in a massive growth of data generated by these devices and sensors put on the Internet. Physical-cyber-social (PCS) big data consist of this IoT data, complemented by relevant Web-based and social data of various modalities. Smart data is about exploiting this PCS big data to get deep insights and make it actionable, and making it possible to facilitate building intelligent systems and applications. This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing. Their synergistic use is expected to power future progress in building intelligent systems …

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Sparse Coding Based Dense Feature Representation Model For Hyperspectral Image Classification, Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li Jan 2015

Sparse Coding Based Dense Feature Representation Model For Hyperspectral Image Classification, Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li

Electrical & Computer Engineering Faculty Publications

We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l1/lq regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear …

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Bio-Inspired Optimization Of Ultra-Wideband Patch Antennas Using Graphics Processing Unit Acceleration, Brian Vyhnalek Jan 2014

Bio-Inspired Optimization Of Ultra-Wideband Patch Antennas Using Graphics Processing Unit Acceleration, Brian Vyhnalek

ETD Archive

Ultra-wideband (UWB) wireless systems have recently gained considerable attention as effective communications platforms with the properties of low power and high data rates. Applications of UWB such as wireless USB put size constraints on the antenna, however, which can be very dicult to meet using typical narrow band antenna designs. The aim of this thesis is to show how bio-inspired evolutionary optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO) can produce novel UWB planar patch antenna designs that meet a size constraint of a 10 mm 10 mm patch. Each potential antenna design …

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Cancer Risk Analysis By Fuzzy Logic Approach And Performance Status Of The Model, Atinç Yilmaz, Kürşat Ayan Jan 2013

Cancer Risk Analysis By Fuzzy Logic Approach And Performance Status Of The Model, Atinç Yilmaz, Kürşat Ayan

Turkish Journal of Electrical Engineering and Computer Sciences

Cancer is the leading life-threatening disease for people in today's world. Although cancer formation is different for each type of cancer, it has been determined by studies and research that stress also triggers cancer types. Early precaution is very important for people who have not fallen ill yet with a disease like cancer that has a high mortality rate and expensive treatment. With this study, we expound that the possibility of developing such disease may be decreased and people could take measures against it. For the 3 cancer types selected as pilot work by introducing a fuzzy logic model, the …

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A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya Jan 2013

A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing …

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Object Retrieval From Secure Unknown Interior Spaces Using Autonomous Unmanned Aerial Vehicles, John Levous, Julie Hoven, Victor Habgood, Abdulrahman Alotaibi, Brandon Ordway, Garibe Mohammed-Jones, Haole Guo, Filip Cuckov, Chung-Hao Chen Jan 2012

Object Retrieval From Secure Unknown Interior Spaces Using Autonomous Unmanned Aerial Vehicles, John Levous, Julie Hoven, Victor Habgood, Abdulrahman Alotaibi, Brandon Ordway, Garibe Mohammed-Jones, Haole Guo, Filip Cuckov, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

This paper describes an autonomous unmanned aerial vehicle (UAV) designed to participate in the 23rd annual International Aerial Robotics Competition. The UAV is equipped with onboard sensors and a Harvard architecture 8-bit RISC microcontroller to monitor and locally control its flight telemetry. Additional sensors (and an additional microcontroller) are used for detecting and mapping of structural and environmental objects while the UAV is in flight. The microcontrollers are interfaced with wireless communication modules for transmitting flight telemetry and structural/environmental data to a ground control station that sends the UAV command and control signals required for the mission objectives. The UAV …

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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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Spatio-Temporal Negotiation Protocols, Yi Luo Jan 2011

Spatio-Temporal Negotiation Protocols, Yi Luo

Electronic Theses and Dissertations

Canonical problems are simplified representations of a class of real world problems. They allow researchers to compare algorithms in a standard setting which captures the most important challenges of the real world problems being modeled. In this dissertation, we focus on negotiating a collaboration in space and time, a problem with many important real world applications. Although technically a multi-issue negotiation, we show that the problem can not be represented in a satisfactory manner by previous models. We propose the "Children in the Rectangular Forest" (CRF) model as a possible canonical problem for negotiating spatio-temporal collaboration. In the CRF problem, …

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Exploiting Opponent Modeling For Learning In Multi-Agent Adversarial Games, Kennard R. Laviers Jan 2011

Exploiting Opponent Modeling For Learning In Multi-Agent Adversarial Games, Kennard R. Laviers

Electronic Theses and Dissertations

An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our …

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Computation Intelligence Method To Find Generic Non-Coding Rna Search Models, Jennifer A. Smith May 2010

Computation Intelligence Method To Find Generic Non-Coding Rna Search Models, Jennifer A. Smith

Electrical and Computer Engineering Faculty Publications and Presentations

Fairly effective methods exist for finding new noncoding RNA genes using search models based on known families of ncRNA genes (for example covariance models). However, these models only find new members of the existing families and are not useful in finding potential members of novel ncRNA families. Other problems with family-specific search include large processing requirements, ambiguity in defining which sequences form a family and lack of sufficient numbers of known sequences to properly estimate model parameters. An ncRNA search model is proposed which includes a collection of non-overlapping RNA hairpin structure covariance models. The hairpin models are chosen from …

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Robust Dialog Management Through A Context-Centric Architecture, Victor C. Hung Jan 2010

Robust Dialog Management Through A Context-Centric Architecture, Victor C. Hung

Electronic Theses and Dissertations

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of …

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Uncertainty Management Of Intelligent Feature Selection In Wireless Sensor Networks, Sanchita Mal-Sarkar Jan 2009

Uncertainty Management Of Intelligent Feature Selection In Wireless Sensor Networks, Sanchita Mal-Sarkar

ETD Archive

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in …

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An Analysis Of Misclassification Rates For Decision Trees, Mingyu Zhong Jan 2007

An Analysis Of Misclassification Rates For Decision Trees, Mingyu Zhong

Electronic Theses and Dissertations

The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by …

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Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj Mar 2006

Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj

Electrical & Computer Engineering Faculty Research

The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …

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Instantaneously Trained Neural Networks With Complex And Quaternion Inputs, Adityan V. Rishiyur Jan 2006

Instantaneously Trained Neural Networks With Complex And Quaternion Inputs, Adityan V. Rishiyur

LSU Master's Theses

Neural network architectures such as backpropagation networks, perceptrons or generalized Hopfield networks can handle complex inputs but they require a large amount of time and resources for the training process. This thesis investigates instantaneously trained feedforward neural networks that can handle complex and quaternion inputs. The performance of the basic algorithm has been analyzed and shown how it provides a plausible model of human perception and understanding of images. The motivation for studying quaternion inputs is their use in representing spatial rotations that find applications in computer graphics, robotics, global navigation, computer vision and the spatial orientation of instruments. The …

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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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Seven Principles Of Efficient Human Robot Interaction, Michael A. Goodrich, Dan R. Olsen Jr. Oct 2003

Seven Principles Of Efficient Human Robot Interaction, Michael A. Goodrich, Dan R. Olsen Jr.

Faculty Publications

Advances in robot technology and artificial intelligence have increased the range of robot applications as well as the importance of supporting human interaction with robots and robot teams. Previous work by the authors has highlighted the importance of creating neglect tolerant autonomy and efficient interfaces. In this paper, lessons learned from evaluating neglect tolerance and interface efficiency are compiled into a set of principles for efficient interaction. Emphasis is placed on designing efficient interfaces, but many of the principles require autonomy levels that support the principles. Each principle is illustrated by an example and motivated by citing relevant factors from …

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A New Approach To Robot’S Imitation Of Behaviors By Decomposition Of Multiple-Valued Relations, Uland Wong, Marek Perkowski Sep 2002

A New Approach To Robot’S Imitation Of Behaviors By Decomposition Of Multiple-Valued Relations, Uland Wong, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Relation decomposition has been used for FPGA mapping, layout optimization, and data mining. Decision trees are very popular in data mining and robotics. We present relation decomposition as a new general-purpose machine learning method which generalizes the methods of inducing decision trees, decision diagrams and other structures. Relation decomposition can be used in robotics also in place of classical learning methods such as Reinforcement Learning or Artificial Neural Networks. This paper presents an approach to imitation learning based on decomposition. A Head/Hand robot learns simple behaviors using features extracted from computer vision, speech recognition and sensors.

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Diagnostics Of Bar And End-Ring Connector Breakage Faults In Polyphase Induction Motors Through A Novel Dual Track Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Modeling, Richard J. Povinelli, John F. Bangura, Nabeel Demerdash, Ronald H. Brown Mar 2002

Diagnostics Of Bar And End-Ring Connector Breakage Faults In Polyphase Induction Motors Through A Novel Dual Track Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Modeling, Richard J. Povinelli, John F. Bangura, Nabeel Demerdash, Ronald H. Brown

Electrical and Computer Engineering Faculty Research and Publications

This paper develops the fundamental foundations of a technique for detection of faults in induction motors that is not based on the traditional Fourier transform frequency domain approach. The technique can extensively and economically characterize and predict faults from the induction machine adjustable speed drive design data. This is done through the development of dual-track proof-of-principle studies of fault simulation and identification. These studies are performed using our proven Time Stepping Coupled Finite Element-State Space method to generate fault case data. Then, the fault cases are classified by their inherent characteristics, so-called “signatures” or “fingerprints.” These fault signatures are extracted …

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Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson Mar 1999

Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson

Electrical and Computer Engineering Faculty Publications and Presentations

"Learning Hardware" approach involves creating a computational network based on feedback from the environment (for instance, positive and negative examples from the trainer), and realizing this network in an array of Field Programmable Gate Arrays (FPGAs). Computational networks can be built based on incremental supervised learning (Neural Net training) or global construction (Decision Tree design). Here we advocate the approach to Learning Hardware based on Constructive Induction methods of Machine Learning (ML) using multivalued functions. This is contrasted with the Evolvable Hardware (EHW) approach in which learning/evolution is based on the genetic algorithm only.

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Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson Jan 1999

Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson

Electrical and Computer Engineering Faculty Publications and Presentations

"Learning Hardware" approach involves creating a computational network based on feedback from the environment (for instance, positive and negative examples from the trainer), and realizing this network in an array of Field Programmable Gate Arrays (FPGAs). Computational networks can be built based on incremental supervised learning (Neural Net training) or global construction (Decision Tree design). Here we advocate the approach to Learning Hardware based on Constructive Induction methods of Machine Learning (ML) using multivalued functions. This is contrasted with the Evolvable Hardware (EHW) approach in which learning/evolution is based on the genetic algorithm only. Various approaches to supervised inductive learning …

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Simulation Study Of Learning Automata Games In Automated Highway Systems, Cem Unsal, Pushkin Kachroo, John S. Bay Nov 1997

Simulation Study Of Learning Automata Games In Automated Highway Systems, Cem Unsal, Pushkin Kachroo, John S. Bay

Electrical & Computer Engineering Faculty Research

One of the most important issues in Automated Highway System (AHS) deployment is intelligent vehicle control. While the technology to safely maneuver vehicles exists, the problem of making intelligent decisions to improve a single vehicle’s travel time and safety while optimizing the overall traffic flow is still a stumbling block. We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible (lateral and longitudinal) actions to avoid collisions. This learning method is capable of …

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Detection And Location Of Pipe Damage By Artificial-Neural-Netprocessed Moire Error Maps, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Scott Christian Cahall May 1993

Detection And Location Of Pipe Damage By Artificial-Neural-Netprocessed Moire Error Maps, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Scott Christian Cahall

Electrical Engineering and Computer Science Faculty Publications

A novel automated inspection technique to recognize, locate, and quantify damage is developed. This technique is based on two already existing technologies: video moire metrology and artificial neural networks. Contour maps generated by video moire techniques provide an accurate description of surface structure that can then be automated by means of neutral networks. Artificial neural networks offer an attractive solution to the automated interpretation problem because they can generalize from the learned samples and provide an intelligent response for similar patterns having missing or noisy data. Two dimensional video moire images of pipes with dents of different depths, at several …

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Design Of An Artificial Neural Network Based Tactile Sensor For The Utah/Mit Dexterous Hand, Jeffery D. Nering Sep 1992

Design Of An Artificial Neural Network Based Tactile Sensor For The Utah/Mit Dexterous Hand, Jeffery D. Nering

Theses and Dissertations

The Neural Tactile Sensor (NTS) is a high resolution, easily manufactured tactile sensor consisting of electrodes, a thin resistive 'skin', and pattern recognition circuitry that is capable of resolving dynamic and static contact location, force, and slip throughout the continuum of the sensor's active region. The sensor operates by means of a resistive 'skin' harboring the electric field generated when a current is injected into it, and a plurality of electrodes for taking measurements of said electric field. When current flows through the resistive medium from the location of tactile contact, an electric field within the resistive medium is established, …

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Hardware-Verification Through Logic Extraction, Michael A. Dukes Mar 1992

Hardware-Verification Through Logic Extraction, Michael A. Dukes

Theses and Dissertations

A Prolog-based system is described which employs logic-extraction to perform hardware-verification. The extraction rules are built automatically from hierarchical structural VHDL models, enabling the equivalence of a structural VHDL description and a layout specification to be verified. Pin-to-pin critical- path analysis is performed within the logic-extraction process; many noncritical paths are pruned early, making pin-to-pin critical path analysis of large circuits feasible. It is demonstrated that a design methodology based on logic extraction, VHDL, and a layout tool can provide a fabricated functionally- correct IC design without circuit-level or switch-level simulation. This methodology is shown to be practical for VLSI …

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Characterization Of Multilayer Perceptrons And Their Application To Multisensor Automatic Target Detection, Dennis W. Ruck Dec 1990

Characterization Of Multilayer Perceptrons And Their Application To Multisensor Automatic Target Detection, Dennis W. Ruck

Theses and Dissertations

The multilayer perceptron was extensively analyzed. A technique for analyzing the multilayer perceptron, the saliency measure, was developed which provides a measure of the importance of inputs. The method was compared to the conventional statistical technique of best features and shown to provide similar rankings of the input. Using the saliency measure, it is shown that the multilayer perceptron effectively ignores useless inputs and that whether it is trained using backpropagation or extended Kalman filtering, the weighting of the inputs is the same. The backpropagation training algorithm is shown to be a degenerate version of the extended Kalman filter. The …

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