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Joint 9th IFSA World Congress and 20th NAFIPS International Conference

作者： Shannon, Thaddeus T. Lendaris, George G. Northwest Computational Intell. Systems Science Ph.D. Program Portland State University P.O. Box 827 Portland OR 97207 United States

In this paper we demonstrate the use of Approximate dynamic programming for the design of a pitch control system for an unstable aircraft using a "hierarchical" tuning approach. The aircraft model is based on an experimental "wave rider" design. We consider the full nonlinear longitudinal model and design a pitch controller for low speed, low altitude flight. Given the unstable, nonlinear nature of our plant, key elements of our implementation are the manner in which we provide "learning experience" for our controller, and the way we target different controller parameters at different points in the tuning process.

关键词： Fuzzy control

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dual heuristic programming for fuzzy control

Dual heuristic programming for fuzzy control

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Joint 9th IFSA World Congress and 20th NAFIPS International Conference

作者： Lendaris, George G. Shannon, Thaddeus T. Schultz, Larry J. Hutsell, Steven Rogers, Alec Northwest Computational Intell. Systems Science Ph.D. Program Portland State University P.O. Box 751 Portland OR 97207 United States

Overview material for the Special Session (Tuning Fuzzy Controllers Using Adaptive Critic Based Approximate dynamic programming) is provided. The dual Heuristic programming (dHP) method of Approximate dynamic programming is described and used to the design a fuzzy control system. dHP and related techniques have been developed in the neurocontrol context but can be equally productive when used with fuzzy controllers or neuro-fuzzy hybrids. This technique is demonstrated by designing a temperature controller for a simple water bath system. In this example, we take advantage of the TSK model framework to initialize the tunable parameters of our plant model with reasonable problem specific values.

关键词： Heuristic programming

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Adaptive critic learning with fuzzy utility

Adaptive critic learning with fuzzy utility

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NAFIPS 2004 - Annual Meeting of the North American Fuzzy Information Processing Society: Fuzzy Sets in the Heart of the Canadian Rockies

作者： Matzner, Shari A. Shannon, Thaddeus T. NW Computational Intelligence Lab. Systems Science Ph.D. Program Portland State University P.O. Box 751 Portland OR 97207 United States

Adaptive Critic methods, which approximate dynamic programming, have been used successfully for solving optimal control problems. The Adaptive Critic learning algorithm optimizes a secondary utility function that is the sum of the present and an future primary utility. The primary utility function measures the instantaneous cost incurred for the last action taken and the resulting state. The motivation for using a fuzzy primary utility function comes from the set of control problems for which there is only a qualitative definition of performance - for example, success or failure. Previous work in applying Adaptive Critic methods to this type of problem showed that a crisp definition of success resulted in solutions that met the control objective, but in an undesirable manner. An appropriate fuzzy utility function, on the other hand, is able to generate the optimal solution. Another motivation for incorporating fuzzy techniques into the utility function is to overcome measurement noise. Measurement noise has a significant adverse effect on the reliability and speed of Adaptive Critic learning;by incorporating fuzzy sets into the utility function, the effect of the noise can be mitigated.

关键词： Fuzzy sets

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Learning with binary-valued utility using derivative adaptive critic methods

Learning with binary-valued utility using derivative adaptiv...

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2004 IEEE International Joint Conference on Neural Networks - Proceedings

作者： Matzner, Shari A. Shannon, Thaddeus T. Lendaris, George G. NW Compl. Intelligence Lab Systems Science Ph.D. Program Portland State University P.O. Box 751 Portland OR 97207 United States

ISBN: (纸本)0780383591

Adaptive Critic methods for reinforcement learning are known to provide consistent solutions to optimal control problems, and are also considered plausible models for cognitive learning processes. This paper discusses binary reinforcement in the context of three Adaptive Critic methods: Heuristic dynamic programming (HdP), dual Heuristic programming (dHP), and Globalized dual Heuristic programming (GdHP). Binary reinforcement arises when the qualitative measure of success is simply "pass" or "fail". We implement binary reinforcement with Adaptive Critic methods for the pole-cart benchmark problem. Results demonstrate two qualitatively dissimilar classes of controllers: those that replicate the system stabilization achieved with quadratic utility, and those that merely succeed at not dropping the pole. It is found that the GdHP method is effective for learning an approximately optimal solution, with results comparable to those obtained via dHP that uses a more informative, quadratic utility function.

关键词： Learning systems

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Book review The Internet in the Workplace , Patricia Wallace , Cambridge University Press , Cambridge, UK ( 2004 ) , 301 pp.

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Technological Forecasting and Social Change 2005年第6期72卷 753-755页

作者： Wayne Wakeland Systems Science Ph.D. Program Portland State University Portland OR 97207 USA

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Radiomics in liver research: A paradigm shift in disease detection and staging

European Journal of Radiology Artificial Intelligence

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European Journal of Radiology Artificial Intelligence 2025年 2卷 100016-100016页

作者： Le, Minh Huu Nhat Kha, Hien Quang Tran, Nghia Minh Nguyen, phat Ky Huynh, Han H. Huynh, phat Kim Lam, Han Le, Nguyen Quoc Khanh International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan AIBioMed Research Group Taipei Medical University Taipei 110 Taiwan Translational Science and Precision Medicine Talent Cultivation Program College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan International Master Program for Translational Science College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan Department of Industrial and Systems Engineering North Carolina A&T State University USA Industrial & Management Systems Engineering Department University of South Florida Tampa FL 33620 USA In-Service Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei 110 Taiwan

AbstractThis narrative review explores radiomics in the broader context of liver disease research, an interdisciplinary field bridging medical imaging, oncology, and data science. It begins with an introduction to data analysis techniques, from foundational methods like descriptive statistics, comparative and regression analysis, and survival analysis, to advanced approaches like machine learning (ML), meta-analysis, and bioinformatics. Contextualizing these methodologies within liver disease research enhances our understanding of liver diseases. The focus is on radiomics, an emerging field, which is first placed in historical context before its applications are explained across fatty liver diseases (FLd), fibrosis, cirrhosis, and liver cancers. The role of radiomics in early detection, disease staging, and outcome prediction, particularly as a non-invasive alternative to liver biopsy, is analyzed in-depth. Current imaging modalities for liver cancer diagnosis are also examined, highlighting their advantages and limitations. Alongside radiomics, these imaging tools enable comprehensive liver assessment. Lastly, the integration of radiomics with other data types, especially genomic data, is explored, emphasizing its role in a holistic approach to liver disease management. The review concludes with a forward-looking perspective on future directions and advancements in radiomics and liver cancer research, addressing the challenges and opportunities in the field. It underscores the transformative impact of these developments on liver cancer research, patient care, personalized medicine, and clinical decision-making.

关键词： AFP alpha-fetoprotein AI artificial intelligence AST aspartate transaminase AUC area under the curve CCrCC clear cell renal cell carcinoma CEUS contrast-enhanced ultrasound CNNs convolutional neural networks dCNN deep convoluted neural network dOR diagnostic odds ratio FLd fatty liver disease GWAS genome-wide association studies HCC hepatocellular carcinoma MAFLd metabolic associated fatty liver disease ML machine learning MRE magnetic resonance elastography NAFLd nonalcoholic fatty liver disease NASH nonalcoholic steatohepatitis NPV negative predictive value PPV positive predictive value VCTE vibration-controlled transient elastography Radiomics Liver disease Cancer Non-invasive diagnostic techniques Presicion medicine deep learning Machine learning

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Using dHP adaptive critic methods to tune a fuzzy automobile steering controller

Using DHP adaptive critic methods to tune a fuzzy automobile...

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Joint 9th IFSA World Congress and 20th NAFIPS International Conference

作者： Schultz, Larry J. Shannon, Thaddeus T. Lendaris, George G. Electrical Engineering Dept. Northwest Computational Intell. Portland State University P.O. Box 751 Portland OR 97207 United States Systems Science Ph.D Program Northwest Computational Intell. Portland State University P.O. Box 751 Portland OR 97207 United States

The approximate dynamic programming method known as dual Heuristic programming (dHP) is applied to the design of a fuzzy controller for a 4-wheel, 2-axle vehicle. This controller is designed to guide an autonomous aut... 详细信息

关键词： Heuristic programming

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Partial, noisy and qualitative models for adaptive critic based neurocontrol

Partial, noisy and qualitative models for adaptive critic ba...

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International Joint Conference on Neural Networks (IJCNN)

作者： T.T. Shannon Systems Science Ph.D. Program Portland State University USA

The roles of plant models in adaptive critic methods for approximate dynamic programming are considered, with primary focus given to the dynamic heuristic programming (dHP) methodology. For complete system identification, partial, approximate, and qualitative models of plant dynamics are considered. Such models are found to be sufficient for successful controller design. As classification is in general easier than regression, the results for qualitative models suggest an avenue for simplifying ongoing system identification in adaptive control applications.

关键词： dynamic programming System identification Adaptive control Control systems Optimal control State estimation Costs programmable control Functional programming Utility programs

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Multi-objective design of synthetic biological circuits

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IFAC-PapersOnLine 2017年第1期50卷 9871-9876页

作者： Lormeau C. Rybiński M. Stelling J. Department of Biosystems Science and Engineering ETH Zurich SIB Swiss Institute of Bioinformatics Life Science Zurich Ph.D. program ”Systems Biology” Basel 4058 Switzerland Department of Biosystems Science and Engineering ETH Zurich SIB Swiss Institute of Bioinformatics Basel 4058 Switzerland

Computational methods enable the design of synthetic biological circuits demonstrating a specific dynamic behavior. Current methods are based on the assembly of parts characterized in different contexts, which often fail to operate as predicted when combined. Here we introduce a circuit design method that compensates for parts uncertainty by identifying circuit topologies whose behavior is robust to variations in parameters. Our heuristic topological filtering approach efficiently yields robust circuit designs in a Bayesian framework, and enables to reliably assess trade-offs between performance, robustness, and experimental feasibility, thus increasing the probability of success of circuit implementation. © 2017

关键词： Biocybernetics Computer-aided circuit design Multiobjective optimisations Robustness Synthetic biology

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Feature-based image capturing and recognition by using active contour models

Chung Cheng Ling Hsueh Pao/Journal of Chung Cheng Institute ...

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Chung Cheng Ling Hsueh Pao/Journal of Chung Cheng Institute of Technology 2012年第1期41卷 57-68页

作者： Kao, Chih-Hong Mao, Ko-Min Hsieh, Lung-Far Aeronautical Systems Research Division Chung-Shan Institute of Science and Technology Taiwan PH.D Program in Management Da-Yeh University Changhua Taiwan School of Management Da-Yeh University Changhua Taiwan

The purpose of establishing a ship recognition system is to research and develop effective ship contour capture in the natural sea environment. We can develop a ship recognition system that is reliable and fast based on a database of ship images. We propose a recognition algorithm for ship images. This system utilizes gradient vector flow to capture the ship image contour first and calculates the geometric eigenvalue using this contour and Fourier descriptor. The eigenvalues are used to perform separated rough and detailed recognition. A graphic user interface is developed and the validity of the proposed technique is demonstrated using identifying images.

关键词： Ships

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