Browsing by Subject "Intelligent control systems"
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Item A machine learning approach to automate classification of literature in a SAM research database(Texas Tech University, 2004-08) Morris, Elizabeth PIn the mid-eighties, researchers at the University of Miami confronted their problem of information overload while investigating information on worker performance. They required literature sources from various fields, such as engineering, business, and psychology, to name a few. To cope with their information overload, they devised a research methodology to partition information resources into category matrices in order to find pattems, frends, or voids. The approach was termed State-of-the-Art Matrix or SAM Analysis. SAM Analysis is a manual process, thus restricting the amount of information for conveying category decisions. During the first phase of the manual process, researchers construct models or categories that best describe the research area. In the next phase, articles from the information sources are read and assigned to the pre-defined categories based on the judgment of assessors. The manual approach presents major challenges to researchers who must deal with identifying and utilizing the information hidden in a large corpus of information. The approach is only practical for a small number of articles and categorization relies on the subjective judgment of assessors. A more scalable and flexible approach, therefore, is needed for categorizing information, such as by using machine leaming and data mining techniques to automate categorization of articles in large volumes of data. In this research, automation is approached through the use of a machine leaming technique known as a Leaming Classifier Systems (LCS). The LCS performs the data mining task of categorizing articles using the SAM approach by utilizing training and testing datasets extracted from SAM EndNote bibliographic databases related to a specific area of research. In order to evaluate the ability of the LCS to predict category membership, accuracy-based metrics borrowed from the field of medicine are applied. The metrics include sensitivity, specificity, positive predictive value, and negative predictive value. After training, the evaluation results indicate that the predictive ability of the LCS system is greater than 90%. The results are obtained during the second trial of a five trial experiment.Item Communication in distributed control telerobotics environments(Texas Tech University, 1998-08) Fu, GengNot availableItem Control of ball and beam with neural networks(Texas Tech University, 1996-05) Eaton, Paul H.The ball-and-beam problem is a benchmark for testing new control algorithms. In the Worid Congress On Neural Networks, 1994, Prof Lotfi Zadeh proposed a more difficult version which he claimed required a fuzzy logic controller. This experiment uses a beam, partially covered with a sticky substance, increasing the difficulty of predicting the ball's motion. We complicated the problem even more by not using any information concerning the ball's velocity. Although it is common to use the first differences of the ball's consecutive positions as a measure of velocity and explicit input to the controller, we preferred to exploit recurrent neural networks inputting only consecutive positions instead. We have used truncated backpropagation through time with the Node-Decoupled Extended Kalman Filter (NDEKF) algorithm to update the weights in the networks. The neurocontroller uses a form of approximate dynamic programming called an adaptive critic design. A hierarchy of such designs exists. Our system uses Dual Heuristic Programming (DHP), an upper-level design. To our best knowledge, our results are the first use of DHP to control a physical system. It is also the first system we know of to meet Zadeh's challenge.Item Development of a novel stopping technique for optimization(Texas Tech University, 1997-12) Iyer, Mahesh SubramaniamNeural networks are being used widely in areas of process control, pattern recognition, etc. The possibility of improving the efficiency of data utilization in neural network training and automating the decision to stop training, using a novel Steady-state Identifier (SSID) algorithm, have been investigated. One conclusion is that complete automation of the decision criterion to stop training is probably beyond the realm of possibility and human judgment seems unavoidable. However, as a beneficial outcome of this study, a technique has been developed to determine the number of neural network training repetitions to guarantee the convergence of the training algorithm within a certain vicinity ofthe global optimum of the objective function, with a desired level of confidence. The concept used is the weakest-link-in-the chain analysis. As another outcome, a novel approach of stopping neural network training has been developed. In this technique, a random fraction ofthe training set data is sampled at each epoch. The error on the random fraction is tested for its attainment of steady-state or otherwise using either a novel Steady-State Identifier or equivalently by visual observation by a human operator. Training is stopped when the error on the random fraction attains Steady-State. This technique, in general, is more cost effective than cross-validation. The overall developments are perfectly general and can also be applied to optimization problems other than neural network training.Item Mechatronic system development and control of an elastic robot testbed(Texas Tech University, 1999-12) Bunaes, Per ChristianRobots and their applications have an ever-increasing importance in our modern society. Areas such as manufacturing and the space industry have been dependent on robots for a number of years. The automotive industry has used robots for repetitive tasks such as spot welding and painting since the 1970's. These applications require relatively little precision but have increased the production and eased the strain on the human body, as robots never get tired of doing the same task over and over again. As little precision was needed in most of the early and still many of the current manufacturing applications, relatively simple control of the robots motion is needed. As our computer technology has evolved exponentially in both speed and memory over the last few decades, we are able to implement more sophisticated control laws for the robots. Hence better accuracy can be achieved. Now, robots are used for detailed applications such as soldering on computer boards. Since better control makes the robots safer for human/robot interaction, the entertainment industry have started to utilize robots in simulators and other devices found in amusement parks.