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International Conference on Neural Information Processing

作者： A. Harati M.N. Ahmadabadi School of Intelligent Systems Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence & AI and Robotics Laboratory Department of Electrical and Computer Engineering University of Tehran Tehran Iran

In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.

关键词： Feedback Intelligent agent Intelligent robots Process control control systems Intelligent control Artificial intelligence Robot kinematics Physics computing Reliability engineering

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Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm.

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International journal of neural systems 2001年第5期11卷 427-443页

作者： Yen, G.G. Meesad, P. Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater 74078 OK United States

In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fisher's Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

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AUTOMATIC FROG CALLS MONITORING SYSTEM: A MACHINE LEARNING APPROACH

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International Journal of Computational Intelligence and Applications 2001年第2期1卷 165-186页

作者： GARY G. YEN QIANG FU Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 USA

Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recognize the vocalizations of four species of frogs and can identify different individuals within the species of interest, is proposed. For the desired monitoring system, species identification is performed first with the proposed filtering and grouping algorithm. Individual identification, which can estimate frog population within the specific species, is performed in the second stage. Digital signal pre-processing, feature extraction, dimensionality reduction, and neural network pattern classification are performed step by step in this stage. Wavelet Packet feature extraction together with two different dimension reduction algorithms are synergistically integrated to produce final feature vectors, which are to be fed into a neural network classifier. The simulation results show the promising future of deploying an array of continuous, on-line environmental monitoring systems based upon nonintrusive analysis of animal calls.

关键词： Frog calls monitoring feature extraction dimensionality reduction pattern classification

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Online fault accommodation control for catastrophic system failures

Online fault accommodation control for catastrophic system f...

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American control Conference (ACC)

作者： Liang-Wei Ho G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University USA

Prompted by the increasing demands in system reliability and survivability, fault detection and accommodation has quickly become one of the most active research areas in the intelligent control community. In this work, the necessary and sufficient conditions for system online stability under catastrophic failures have been derived based upon discrete-time Lyapunov stability theory. An online fault accommodation control framework is proposed to deal with the desired trajectory-tracking problem for systems suffering from various unknown and unanticipated catastrophic component failures. The approach is to continuously monitor the system performance and identify the system's current state by using an efficient online fault detection scheme based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, a neural network is suggested as the online estimator to learn and approximate the unknown system failure dynamics. Effective control signals to accommodate the dynamic failures are then computed through the realization of the neural network estimator for the unknown failures based only upon the partially available information of the faults.

关键词： control systems Fault detection Neural networks Reliability Intelligent control Sufficient conditions Stability Lyapunov method Condition monitoring System performance

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Applications of coordinate logic filters in image analysis and pattern recognition

Applications of coordinate logic filters in image analysis a...

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International Symposium on Image and Signal Processing and Analysis

作者： B.G. Mertzios K. Tsirikolias Automatic Control Systems Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

ISBN: (纸本)9539676940

The paper presents a number of image processing and pattern recognition applications using coordinate logic filters which execute coordinate logic operations among the pixels of the image. These filters are very efficient in various 1D, 2D or higher-dimensional digital signal processing applications, such as noise removal, magnification, opening, closing, skeletonization, coding, edge detection, feature extraction, and fractal modeling. The key issue in the coordinate logic analysis of images is the method of fast successive filtering and managing of the residues. The desired processing is achieved by executing only direct logic operations among the pixels of the given image. Coordinate logic filters can be easily and quickly implemented using logic circuits or cellular automata; this is their primary advantage.

关键词： Image analysis Image edge detection Pixel Logic circuits Image processing Pattern recognition Digital filters Digital signal processing Image coding Feature extraction

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A hybrid intelligent system for medical diagnosis

A hybrid intelligent system for medical diagnosis

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International Joint Conference on Neural Networks (IJCNN)

作者： P. Meesad G.G. Yen School of Electrical and Computer Engineering Intelligent Systems and Control Laboratory Oklahoma State University Stillwater OK USA

ISBN: (纸本)0780370449

We propose a novel hybrid intelligent system (HIS) that is a combination of numerical and linguistic knowledge representation. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a fuzzy linguistic model optimized via the genetic algorithm. The ILFN is self-organizing network with the capability of fast, online, incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy and comprehensibility. The resulted HIS is capable of dealing with low-level numerical computation and higher-level linguistic computation. After the system completely constructed, it can incrementally learn new information in both numerical and linguistic structures. To evaluate the system's performance the well-known benchmark Wisconsin breast cancer data was studied as an application to medical diagnosis. The simulation results show that the proposed HIS perform better than the individual standalone systems.

关键词： Hybrid intelligent systems Medical diagnosis Fuzzy neural networks Knowledge representation Genetic algorithms Self-organizing networks System performance Breast cancer Computational modeling Medical simulation

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Coordination of exploration and exploitation in a dynamic environment

Coordination of exploration and exploitation in a dynamic en...

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International Joint Conference on Neural Networks (IJCNN)

作者： G. Yen Fengming Yang T. Hickey M. Goldstein School of Electrical and Computer Engineering Intelligent Systems and Control Laboratory Oklahoma State University Stillwater OK USA

ISBN: (纸本)0780370449

One much researched issue in reinforcement learning is the trade off between exploration and exploitation. Being able to effectively balance exploration and exploitation activities becomes even more crucial in a dynamic environment. An algorithm is proposed herein that provides one solution to the exploration vs. exploitation dilemma. The algorithm is presented in the context of a path-finding agent in a dynamic grid-world problem. The state-value function used is penalty based, allowing the agent to act over the space of paths with minimal penalties. A forgetting mechanism is implemented that allows the agent to explore paths that were previously determined to be suboptimal. Simulation results are used to analyze the behavior of the proposed algorithm in a dynamic environment.

关键词： Learning systems Genetic algorithms systems engineering and theory Intelligent systems Intelligent control control systems Analytical models Algorithm design and analysis Heuristic algorithms Acceleration

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Reducing OS overhead for real-time industrial controllers with adjustable timer resolution

Reducing OS overhead for real-time industrial controllers wi...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Tae Rim Park Jae Hyun Park Wook Hyun Kwon Control Information Systems Laboratory School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea Real-time Embedded Systems Laboratory School of Information and Communication Engineering Inha University Incheon South Korea

ISBN: (纸本)0780370902

In this paper, a new method, named adjustable timer resolution (ATR) is proposed to control tasks in a timely manner, with enhanced performance on real-time operating systems. It prevents useless execution of the timer interrupt service routine (ISR). Features of the proposed method are compared with the conventional method, and the schedulability is analyzed to estimate the performance. As an implementation model, uC/OS-II is modified and ported to a power PC processor system. The metrics and results of the model are presented and discussed.

关键词： Industrial control Real time systems control systems Operating systems Communication system control Performance analysis Embedded system Processor scheduling Power system modeling Time factors

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Robust PI and PID controllers including Smith predictor structure

Robust PI and PID controllers including Smith predictor stru...

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American control Conference (ACC)

作者： B. Kristiansson B. Lennartson Department of Electrical and Computer Engineering Chalmers Lindholmen Goteborg Sweden Department of Signals and Systems Control and Automation Laboratory Chalmers University of Technology Goteborg Sweden

In this paper PI and PID controllers augmented by a Smith predictor structure are compared to ordinary PI and PID controllers, for plants with medium and great time delays. The comparison is based an a systematic evaluation method, where both robustness and performance aspects in different frequency regions are taken into account. The results show that a Smith predictor increases performance somewhat for a PID controller when the time delay is moderate. For great time delays, however, the improvement is insignificant. An important remark is that it is necessary to include the predictor structure in the design to get acceptable robustness properties. For a PI controller the benefits from a predictor are mostly small or negligible independent of time delay. Most important is, however that a derivative part obviously will bring significantly greater improvement than a Smith predictor to a system with a PI controller. This result is somewhat surprising, since Smith predictors are normally used together with PI and not with PID controllers. Furthermore, it is sometimes argued that it is no use to include derivative action for plants with significant time delay.

关键词： Robust control Three-term control control systems Delay effects Automatic control Frequency Robustness Robust stability Automation Pi control

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Active suspension design based on mixed uncertainty modelling 6

Active suspension design based on mixed uncertainty modellin...

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6th European control Conference, ECC 2001

作者： Gaspar, P. Szaszi, I. Balas, G.J. Bokor, J. Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17 Budapest Hungary Department of Control and Transport Automation Budapest University of Technology and Economics Muegyetem rkp. 3 Budapest Hungary Department of Aerospace Engineering and Mechanics University of Minnesota 107 Akerman Hall 110 Union St. SE MN United States

ISBN: (纸本)9783952417362

This paper proposes the mixed lì synthesis for active suspension problems. With this method, a robust compensator that achieves nominal performance and meets robust stability specifications can be designed. Moreover, both the real parametric and the complex uncertainties are treated together, which usually yields a less conservative compensator than the traditional robust methods. In this paper, the design strategy is applied for active suspension, which involves real parametric uncertainties. The results are compared with the traditional LQG, complex lì and mixed lì methods. © 2001 EUCA.

关键词： Robust control

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