A hierarchical fuzzy expert system is proposed for multispectral Landsat image classification to overcome difficulties with conventional maximum-likelihood classifier (MLC) based on normal distribution and easily inco...
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A hierarchical fuzzy expert system is proposed for multispectral Landsat image classification to overcome difficulties with conventional maximum-likelihood classifier (MLC) based on normal distribution and easily incorporate other collateral data, such as vegetation index, digital elevation model, etc. The hierarchical structure is to reduce fuzzy rules to incorporate as many useful data sources as possible. Adaptive-Neural-Network Based Fuzzy Inference System (ANFIS) is used to build up fuzzy rule based systems to adapt training data. The expert system is tested for the classification on Landsat 7 ETM+ image and results are effective for multispectral image classification.
Magnetic resonance imaging (MRI) is a widely used approach to obtaining high quality medical images of the brain. Post-processing MRI images with segmentation algorithms enhances the visualization and measurement of s...
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ISBN:
(纸本)188933524X
Magnetic resonance imaging (MRI) is a widely used approach to obtaining high quality medical images of the brain. Post-processing MRI images with segmentation algorithms enhances the visualization and measurement of soft tissues and lesions. Segmented brain images contain information amenable to quantitative analysis (e.g., tissue component percentage in a region of interest (ROI)) and diagnostic interpretation (e.g., total lesion volume). A number of different segmentation algorithms have been developed for this purpose. In this paper, we propose a novel automated segmentation technique, hierarchical structure weighted probabilistic neural network (HSWPNN), based on multi-scale feature extraction, hierarchical labeling structure, and a modified weighted probabilistic neural network (PNN). Compared to other clustering algorithms, our method is relatively robust to noise and accurate. We compare our results to a model of ground truth.
This paper provides a model predictive approach to control switched reluctance motors (SRM's). A local linear neuro-fuzzy model is used to model SRM. Then a predictive control schema is devised considering an appr...
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In this paper a model reference variable structure controller (VSC) for an active suspension system is designed. A half vehicle model is used in which, the vertical and pitch motions of the mass supported by the suspe...
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The objective is to emulate the stimulus-response behavior for robot navigation in unknown environments. The robot's objective is to move from its current position to some predefined coordinates, namely, a predefi...
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The objective is to emulate the stimulus-response behavior for robot navigation in unknown environments. The robot's objective is to move from its current position to some predefined coordinates, namely, a predefined goal point. The proposed behavior is reactive and the robot has no prior information about the size and location of the obstacles in the environment. This control algorithm is a combination of two main behaviors: wall following and goal seeking, and is based totally on real-time data acquisition from immediate surroundings of the robot. A carefully designed fuzzy inference system (FlS) is used to determine the change in velocity and direction of the robot based on the sonar sensor and inertial cube (Gyro) sensor readings.
Robust asymptotic stability for hybrid systems is considered. For this purpose, a generalized solution concept is developed. The first step is to characterize a hybrid time domain that permits an efficient description...
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Robust asymptotic stability for hybrid systems is considered. For this purpose, a generalized solution concept is developed. The first step is to characterize a hybrid time domain that permits an efficient description of the convergence of a sequence of solutions. Graph convergence is used. Then a generalized solution definition is given that leads to continuity with respect to initial conditions and perturbations of the system data. This property enables new results on necessary conditions for asymptotic stability in hybrid systems.
A distributed control system (DCS) is developed and hardware verified for a class of digitally controlled modular DC-DC converters. The converters are each independently controlled by its own FPGA-based digital contro...
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In this paper cardiovascular dynamics, which refers to the dynamic relationship among the heart rate (HR), arterial blood pressure (ABP) and instantaneous lung volume (ILV), is identified through a novel combination a...
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In this paper cardiovascular dynamics, which refers to the dynamic relationship among the heart rate (HR), arterial blood pressure (ABP) and instantaneous lung volume (ILV), is identified through a novel combination approach that consists of a set of linear auto-regression (AR) equations and nonlinear fuzzy-neural inference. Based on linear assumption of cardiovascular system, auto-regressive and moving average method (ARMA) has been popular approaches to identify the complex cardio-system in recent years. Fuzzy set theory is very suitable to systems with uncertainties such as the cardiovascular dynamic system with expert knowledge. Fuzzy- Neural inference paradigm imports the auto-learning property into fuzzy logic engine, therefore extracts some knowledge from data automatically. An effective hybrid approach, which has parallel modular structure of AR and Fuzzy-neural inference, becomes feasible IO interpret physiologically linear component of heart function and nonlinear nervous regulation component respectively. Details of proposed combination method as well as subjects' study results are presented in this paper.
Using unbounded time-varying scaling of the states we design C/sup 1/ feedback laws for power integrator triangular systems which globally asymptotically stabilize (GAS) the origin despite the uncontrollability of the...
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Using unbounded time-varying scaling of the states we design C/sup 1/ feedback laws for power integrator triangular systems which globally asymptotically stabilize (GAS) the origin despite the uncontrollability of the linearization. With bounded scaling the feedback laws achieve global practical stability (GPS). For a trade-off between GAS/GPS of the origin and unboundedness/boundedness of the scaling we construct a dynamic version of these feedback laws.
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