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Pages
- Title
- An intelligent approach to system identification.
- Creator
- Saravanan, Natarajan, Florida Atlantic University, Duyar, Ahmet, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
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System identification methods are frequently used to obtain appropriate models for the purpose of control, fault detection, pattern recognition, prediction, adaptive filtering and other purposes. A number of techniques exist for the identification of linear systems. However, real-world and complex systems are often nonlinear and there exists no generic methodology for the identification of nonlinear systems with unknown structure. A recent approach makes use of highly interconnected networks...
Show moreSystem identification methods are frequently used to obtain appropriate models for the purpose of control, fault detection, pattern recognition, prediction, adaptive filtering and other purposes. A number of techniques exist for the identification of linear systems. However, real-world and complex systems are often nonlinear and there exists no generic methodology for the identification of nonlinear systems with unknown structure. A recent approach makes use of highly interconnected networks of simple processing elements, which can be programmed to approximate nonlinear functions to identify nonlinear dynamic systems. This thesis takes a detailed look at identification of nonlinear systems with neural networks. Important questions in the application of neural networks for nonlinear systems are identified; concerning the excitation properties of input signals, selection of an appropriate neural network structure, estimation of the neural network weights, and the validation of the identified model. These questions are subsequently answered. This investigation leads to a systematic procedure for identification using neural networks and this procedure is clearly illustrated by modeling a complex nonlinear system; the components of the space shuttle main engine. Additionally, the neural network weights are determined by using a general purpose optimization technique known as evolutionary programming which is based on the concept of simulated evolution. The evolutionary programming algorithm is modified to include self-adapting step sizes. The effectiveness of the evolutionary programming algorithm as a general purpose optimization algorithm is illustrated on a test suite of problems including function optimization, neural network weight optimization, optimal control system synthesis and reinforcement learning control.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/12371
- Subject Headings
- Neural networks (Computer science), System identification, Nonlinear theories, System analysis, Space shuttles--Electronic equipment, Algorithms--Computer programs
- Format
- Document (PDF)
- Title
- Sensitivity analysis of blind separation of speech mixtures.
- Creator
- Bulek, Savaskan., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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Blind source separation (BSS) refers to a class of methods by which multiple sensor signals are combined with the aim of estimating the original source signals. Independent component analysis (ICA) is one such method that effectively resolves static linear combinations of independent non-Gaussian distributions. We propose a method that can track variations in the mixing system by seeking a compromise between adaptive and block methods by using mini-batches. The resulting permutation...
Show moreBlind source separation (BSS) refers to a class of methods by which multiple sensor signals are combined with the aim of estimating the original source signals. Independent component analysis (ICA) is one such method that effectively resolves static linear combinations of independent non-Gaussian distributions. We propose a method that can track variations in the mixing system by seeking a compromise between adaptive and block methods by using mini-batches. The resulting permutation indeterminacy is resolved based on the correlation continuity principle. Methods employing higher order cumulants in the separation criterion are susceptible to outliers in the finite sample case. We propose a robust method based on low-order non-integer moments by exploiting the Laplacian model of speech signals. We study separation methods for even (over)-determined linear convolutive mixtures in the frequency domain based on joint diagonalization of matrices employing time-varying second order statistics. We investigate the sources affecting the sensitivity of the solution under the finite sample case such as the set size, overlap amount and cross-spectrum estimation methods.
Show less - Date Issued
- 2010
- PURL
- http://purl.flvc.org/FAU/2953201
- Subject Headings
- Blind source separation, Mathematical models, Signal processing, Digital techniques, Neural networks (Computer science), Automatic speech recognition, Speech processing systems
- Format
- Document (PDF)
- Title
- Artificial neural network prediction of ground-level ozone concentration in Palm Beach County.
- Creator
- Crumiere, Mylene., Florida Atlantic University, Scarlatos, Panagiotis (Pete) D.
- Abstract/Description
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The purpose of this study was to develop a user-friendly mathematical model for prediction of daily, ground level ozone concentration in Palm Beach County, Florida. The focus of this project was to investigate the correlation between hourly ozone concentrations and pre-existing pollutant levels and meteorological data. An artificial neural network model was applied, involving a backpropagation algorithm and the tangent sigmoid as the transfer function. Surface meteorological data and upper...
Show moreThe purpose of this study was to develop a user-friendly mathematical model for prediction of daily, ground level ozone concentration in Palm Beach County, Florida. The focus of this project was to investigate the correlation between hourly ozone concentrations and pre-existing pollutant levels and meteorological data. An artificial neural network model was applied, involving a backpropagation algorithm and the tangent sigmoid as the transfer function. Surface meteorological data and upper air data such as pressure, temperature, dew point temperature, wind speed and wind direction were included in the model, along with the ozone concentration in the hour previous to the forecast. Based on the model results, the 8-hour average ozone concentration is to be forecasted. This will assist state and local air pollution officials in providing the general public with early notice of an impending air quality problem.
Show less - Date Issued
- 1999
- PURL
- http://purl.flvc.org/fcla/dt/15723
- Subject Headings
- Neural networks (Computer science), Air--Pollution--Mathematical models, Air--Pollution--Florida--Palm Beach County, Ozone--Forecasting
- Format
- Document (PDF)
- Title
- Prediction of crude oil product quality parameters using neural networks.
- Creator
- Bawazeer, Khalid Ahmed., Florida Atlantic University, Zilouchian, Ali
- Abstract/Description
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Inferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy...
Show moreInferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy neural network model is also proposed that takes into account the fuzziness in both process variables and the corresponding product quality parameter. The training algorithm is derived based on the backpropagation technique. The results of the proposed study can ultimately enhance the on-line prediction of crude oil product quality parameters for crude fractionation processes in the Ras Tanura Oil Refinery.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/15302
- Subject Headings
- Petroleum products--Analysis, Petroleum products--Testing, Petroleum industry and trade--Quality control, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States.
- Creator
- Lloyd, Eric, Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from...
Show moreA self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004764, http://purl.flvc.org/fau/fd/FA00004764
- Subject Headings
- Cognitive neuroscience., Neural networks (Computer science), Pattern recognition systems., Artificial intelligence., Self-organizing systems., Human-computer interaction., Human information processing.
- Format
- Document (PDF)
- Title
- A VLSI implementable learning algorithm.
- Creator
- Ruiz, Laura V., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
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A top-down design methodology using hardware description languages (HDL's) and powerful design, analysis, synthesis and layout software tools for electronic circuit design is described and applied to the design of a single layer artificial neural network that incorporates on-chip learning. Using the perception learning algorithm, these simple neurons learn a classification problem in 10.55 microseconds in one application. The objective is to describe a methodology by following the design of a...
Show moreA top-down design methodology using hardware description languages (HDL's) and powerful design, analysis, synthesis and layout software tools for electronic circuit design is described and applied to the design of a single layer artificial neural network that incorporates on-chip learning. Using the perception learning algorithm, these simple neurons learn a classification problem in 10.55 microseconds in one application. The objective is to describe a methodology by following the design of a simple network. This methodology is later applied in the design of a novel architecture, a stochastic neural network. All issues related to algorithmic design for VLSI implementability are discussed and results of layout and timing analysis given over software simulations. A top-down design methodology is presented, including a brief introduction to HDL's and an overview of the software tools used throughout the design process. These tools make it possible now for a designer to complete a design in a relative short period of time. In-depth knowledge of computer architecture, VLSI fabrication, electronic circuits and integrated circuit design is not fundamental to accomplish a task that a few years ago would have required a large team of specialized experts in many fields. This may appeal to researchers from a wide background of knowledge, including computer scientists, mathematicians, and psychologists experimenting with learning algorithms. It is only in a hardware implementation of artificial neural network learning algorithms that the true parallel nature of these architectures could be fully tested. Most of the applications of neural networks are basically software simulations of the algorithms run on a single CPU executing sequential simulations of a parallel, richly interconnected architecture. This dissertation describes a methodology whereby a researcher experimenting with a known or new learning algorithm will be able to test it as it was intentionally designed for, on a parallel hardware architecture.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/12453
- Subject Headings
- Integrated circuits--Very large scale integration--Design and construction, Neural networks (Computer science)--Design and construction, Computer algorithms, Machine learning
- Format
- Document (PDF)
- Title
- Time-frequency classification of gamma oscillatory activity in the frontoparietal system during working memory.
- Creator
- Romano, Tracy A., Bressler, Steven L., Florida Atlantic University, Charles E. Schmidt College of Science, Center for Complex Systems and Brain Sciences
- Abstract/Description
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Working memory (WM) is a process that allows for the temporary and limited storage of information for an immediate goal or to be stored into a more permanent system. A large number of studies have led to the widely accepted view that WM is mediated by the frontoparietal network (FPN), consisting of areas in the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Current evidence suggests that task specific patterns of neuronal oscillatory activity within the FPN play a fundamental...
Show moreWorking memory (WM) is a process that allows for the temporary and limited storage of information for an immediate goal or to be stored into a more permanent system. A large number of studies have led to the widely accepted view that WM is mediated by the frontoparietal network (FPN), consisting of areas in the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Current evidence suggests that task specific patterns of neuronal oscillatory activity within the FPN play a fundamental role in WM, and yet specific spatio-temporal properties of this activity are not well characterized. This study utilized multisite local field potential (LFP) data recorded from PFC and PPC sites in two macaque monkeys trained to perform a rule-based, Oculomotor Delayed Match-to-Sample task. The animals were required to learn which of two rules determined the correct match (Location matching or Identity matching). Following a 500 ms fixation period, a sample stimulus was presented for 500 ms, followed by a randomized delay lasting 800-1200 ms in which no stimulus was present. At the end of the delay period, a match stimulus was presented, consisting of two of three possible objects presented at two of three possible locations. When the match stimulus appeared, the monkey made a saccadic eye movement to the target. The rule in effect determined which object served as the target. Time-frequency plots of three spectral measures (power, coherence, and Wiener Granger Causality (WGC) were computed from MultiVariate AutoRegressive LFP time-series models estimated in a 100-ms window that was slid across each of three analysis epochs (fixation, sample, and delay). Low (25- 55 Hz) and high gamma (65- 100 Hz) activity were investigated separately due to evidence that they may be functionally distinct. Within each epoch, recording sites in the PPC and PFC were classified into groups according to the similarity of their power t-f plots derived by a K-means clustering algorithm. From the power-based site groups, the corresponding coherence and WGC were analyzed. This classification procedure uncovered spatial, temporal, and frequency dynamics of FPN involvement in WM and other co-occurring processes, such as sensory and target related processes. These processes were distinguishable by rule and performance accuracy across all three spectral measures- power, coherence, and WGC. Location and Identity rule were distinguishable by the low and high-gamma range.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00004157
- Subject Headings
- Brain--Physiology., Biological rhythms., Attention--Physiological aspects., Cognitive neuroscience., Memory--Age factors., Short-term memory., Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- A min/max algorithm for cubic splines over k-partitions.
- Creator
- Golinko, Eric David, Charles E. Schmidt College of Science, Department of Mathematical Sciences
- Abstract/Description
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The focus of this thesis is to statistically model violent crime rates against population over the years 1960-2009 for the United States. We approach this question as to be of interest since the trend of population for individual states follows different patterns. We propose here a method which employs cubic spline regression modeling. First we introduce a minimum/maximum algorithm that will identify potential knots. Then we employ least squares estimation to find potential regression...
Show moreThe focus of this thesis is to statistically model violent crime rates against population over the years 1960-2009 for the United States. We approach this question as to be of interest since the trend of population for individual states follows different patterns. We propose here a method which employs cubic spline regression modeling. First we introduce a minimum/maximum algorithm that will identify potential knots. Then we employ least squares estimation to find potential regression coefficients based upon the cubic spline model and the knots chosen by the minimum/maximum algorithm. We then utilize the best subsets regression method to aid in model selection in which we find the minimum value of the Bayesian Information Criteria. Finally, we preent the R2adj as a measure of overall goodness of fit of our selected model. We have found among the fifty states and Washington D.C., 42 out of 51 showed an R2adj value that was greater than 90%. We also present an overall model of the United States. Also, we show additional applications our algorithm for data which show a non linear association. It is hoped that our method can serve as a unified model for violent crime rate over future years.
Show less - Date Issued
- 2012
- PURL
- http://purl.flvc.org/FAU/3342107
- Subject Headings
- Spline theory, Data processing, Bayesian statistical decision theory, Data processing, Neural networks (Computer science), Mathematical statistics, Uncertainty (Information theory), Probabilities, Regression analysis
- Format
- Document (PDF)
