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Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on

Date 11-11 Sept. 1996

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  • Proceedings of the Fifth IEEE International Conference on Fuzzy Systems Fuzz-IEEE '96 [front matter]

    Publication Year: 1996 , Page(s): i - xxxi
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  • A fuzzy algorithm for pixel classification based on the discrepancy norm

    Publication Year: 1996 , Page(s): 2007 - 2012 vol.3
    Cited by:  Papers (4)
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    A fuzzy method for a particular kind of pixel classification is proposed. It is one of the most important results in the development of an inspection system for a silk-screen printing process. The classification algorithm is applied to a reference image in the initial step of the printing process in order to obtain the regions which are to be checked by applying different criteria. Tight limitations in terms of computation speed have necessitated very specific, efficient methods which operate locally. These methods are motivated and discussed in detail. View full abstract»

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  • Fuzz-IEEE Author Index

    Publication Year: 1996 , Page(s): A
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    Freely Available from IEEE
  • A reinforcement learning fuzzy controller for set-point regulator problems

    Publication Year: 1996 , Page(s): 2136 - 2142 vol.3
    Cited by:  Papers (2)  |  Patents (1)
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    Intelligent control refers to controllers that can analyze their performance and make necessary changes to their behavior in order to satisfy certain predefined control goals. This paper describes a self-learning controller model that can efficiently learn the control law for complex systems through reinforcement learning techniques and dynamic programming-like algorithms. The controller is applied to a class of problems called general set-point regulator problems in which the objective is to drive the system to the set-point while optimizing some performance objective function, making no a priori assumptions about the dynamics of the plant or its optimal trajectory. The relevant tasks for a self-learning controller are discussed. Learning is accomplished via incremental, online dynamic programming-like algorithms. Both temporal differences and Q-learning are used in the learning algorithm. Experimental results with both are reported on the inverted pendulum balancing problem, the power system stabilization problem, and the tethered satellite system retrieval problem View full abstract»

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  • Using genetic algorithms for λ-fuzzy measure fitting and extension

    Publication Year: 1996 , Page(s): 1871 - 1874 vol.3
    Cited by:  Papers (1)
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    Constructing fuzzy measures in systems is an important topic in system research. Revising a set function to be a desirable fuzzy measure is one of the practicable strategies of the construction. In this paper, the following fitting problem is investigated: given a universal set and a set function, which is not necessarily a λ-fuzzy measure, defined on a class of subsets of the universal set, we want to find a regular λ-fuzzy measure on the power set of the universal set such that it is as close as possible to the original set function. This is, essentially, an optimization problem. A genetic algorithm is used to search the optimal solution. As a special case, when the set function is already a regular λ-fuzzy measure on the original domain that is a proper subclass of the power set we can obtain a regular λ-fuzzy measure extension on the power set View full abstract»

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  • A fuzzy rule-based system for labelling the structures in 3D human brain magnetic resonance images

    Publication Year: 1996 , Page(s): 1978 - 1982 vol.3
    Cited by:  Papers (5)
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    In biomedical research, it is desirable to label the brain structures of interest in magnetic resonance (MR) images for quantitative and qualitative analysis. However, the exact location of specific structures in the brain varies significantly from person to person. We have developed a fuzzy rule-based labelling system which incorporates the anatomic knowledge of experts concerning the spatial relationship between different brain landmarks for identifying several major sulci. The objective was to use the identified sulci to automatically locate the frontal lobe of the brain. A number of spatial attributes, including positions, orientation and length, were used in the fuzzy rules. A flattened 2D sulcal map of the brain surface was created from a 3D MR image to represent the locations of the sulci on the brain surface. Two human brain image sets were processed by our fuzzy system and the frontal lobes in both cases were correctly located. The brain images, along with the identified brain structures, were rendered by isosurface for better visualisation View full abstract»

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  • Adaptive weighted fuzzy mean filter

    Publication Year: 1996 , Page(s): 2110 - 2116 vol.3
    Cited by:  Patents (1)
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    A new fuzzy filter for the removal of additive impulse noise, called the adaptive weighted fuzzy mean (AWFM) filter, is proposed and analyzed in this paper. The AWFM is an extensive version of weighted fuzzy mean (WFM) filter and its filtered output signal is either the output of WFM or the processed signal by the detection algorithm. This filter gives very superior performance compared with conventional filters when evaluated by mean absolute error (MAE), mean square error (MSE) and subjective evaluation criteria. For dedicated hardware implementation, AWFM is also much simpler than the conventional median filter View full abstract»

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  • Development and performance analysis of a class of intelligent target recognition algorithms

    Publication Year: 1996 , Page(s): 1996 - 2002 vol.3
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    This paper develops and compares two fuzzy logic based- and a traditional rule-based pattern recognition systems, which perform target recognition with data from a typical range and Doppler resolving radar. The parameters used are target altitude, velocity, range from nearest base, and radar cross section. The systems identify four classes of aircraft: fighter/interceptors, large bombers, rotary craft, and vertical take off and landing (VTOL) combat aircraft. The first fuzzy based technique classifies targets by selecting the aircraft with the maximum summed amount of membership, giving a classification accuracy of 94% (average). The second approach classifies targets by selecting the aircraft through a max-min fuzzy decision system. This results in a 99% average accurate classification. The traditional rule-based method implements an expert system and correctly classifies 75% (average) of the targets View full abstract»

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  • Select and split fuzzy cell Hough transform-a fast and efficient method to detect contours in images

    Publication Year: 1996 , Page(s): 1892 - 1898 vol.3
    Cited by:  Papers (1)
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    In this paper a new variation of Hough transform is proposed. The parameter space of Hough Transform is iteratively split into fuzzy cells which are defined as fuzzy numbers. Each fuzzy cell corresponds to a fuzzy curve in the spatial domain. After each iteration the fuzziness of the cells is reduced and the curves are estimated with better accuracy. The uncertainty of the contour point location is transferred to the parameter space and gives better accuracy in curve estimation than the classical Hough transform, especially when noisy images have to be used. Moreover, the computation time is significantly decreased, since the regions of the parameter space where contours do not correspond, are rejected during the iterations View full abstract»

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  • Simultaneous learning of rules and linguistic terms

    Publication Year: 1996 , Page(s): 1743 - 1749 vol.3
    Cited by:  Papers (8)
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    This paper proposes a linguistic modeling method based on a weighted fuzzy rule base and the associated learning algorithm. The fuzzy reference sets and the rule base are simultaneously identified from numeric data in opposition to many other linguistic methods that divide the identification problem into two separate subtasks. No assumption is made on the number of reference sets that may be irregularly distributed according to the training set. Two numeric examples are presented, the first one concerns function approximation and the second one deals with the prediction of Mackey-Glass time series View full abstract»

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  • Optimal fuzzy rule bases-the cat and mouse problem

    Publication Year: 1996 , Page(s): 1865 - 1870 vol.3
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    Approximate fuzzy rule-based models are more precise if their size is bigger. A larger model, however, requires more time for its evaluation and hence the problem arises of finding a compromise between size and accuracy for the task at hand. This trade-off between computation time and precision is mapped into the problem of tracking a moving target: higher accuracy results in a tighter precision of the target location, but at the cost of longer computation time, during which the target can move further away, thus ultimately requiring a longer search time for target localization. This paper examines the problem of determining the optimal rule-base size that will yield a minimum total time required to repeatedly re-acquire a moving target. The general problem has no known solution: here solutions of specific cases are presented View full abstract»

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  • Agreement: a new perspective of the fuzzy logic foundations

    Publication Year: 1996 , Page(s): 2103 - 2109 vol.3
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    In this paper, some aspects of the human reasoning process, such as commonsense knowledge, uncertainty and approximate reasoning are discussed. A new way to approach these concepts-the point of view of agreement-and the relationships among them are addressed. It is shown that the concept of agreement provides a framework for the development of a formal and sound explanation for concepts (e.g. fuzzy sets) which lack formal semantics. Based on the notion of agreement, a multi-valued logic-logic of agreement-that has been proved to be sound, was developed View full abstract»

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  • Information engineering and fuzzy logic

    Publication Year: 1996 , Page(s): 1525 - 1531 vol.3
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    Information engineering constitutes a variety of tasks related to: 1) information processing (data clarification, enhancing, classification, fusion, summarization, and modelling); 2) information retrieving (through querying and reasoning); and 3) information exploitation (for making decision, designing and optimizing). These tasks are becoming increasingly important with the confluence of computer and communication technologies, e.g. on the Internet. Fuzzy set methods offer useful tools for handling these tasks due to their ability to provide a qualitative interface with data and to model graded notions such as uncertainty, preference and similarity, which play a key role in reasoning and decision. We discuss how fuzzy set techniques can contribute to most of the information engineering tasks due to the fuzzy set representation capabilities and their computational facilities. The paper emphasizes the centrality of information and points out the role of fuzzy sets in different information engineering tasks and application areas View full abstract»

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  • Implementation of fuzzy cluster filter for nonlinear signal and image processing

    Publication Year: 1996 , Page(s): 2117 - 2122 vol.3
    Cited by:  Papers (3)
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    A nonlinear filter known as fuzzy cluster filter (FCF) is introduced. This filter can be used for different signal and image processing applications. The formulation of this filter is based on applying fuzzy clustering to a subset of the signal (or image) and finding the best candidate (i.e., the cluster prototype) for the output. The local statistics of the signal are used for learning the membership functions. Also, the performance of the filter is found for different signal to noise ratios (SNR) by using Monte Carlo simulations View full abstract»

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  • Intelligent highway by fuzzy logic: congestion detection and traffic control on multi-lane roads with variable road signs

    Publication Year: 1996 , Page(s): 1832 - 1837 vol.3
    Cited by:  Papers (5)
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    Traffic control involves analysis of both detected traffic flow and environmental conditions. Due to individual behavior of traffic participants, and the inherent uncertainty of weather condition interpretations, a mathematical model can not fully describe traffic control problems. The paper discourses two aspects of a traffic management system for multi-lane highways with variable road signs. First, fuzzy logic is used to take into account the uncertainties of traffic data, and to detect traffic congestion in isolated road sections. Second, a traffic control approach using a fuzzy model based on experience is shown. Both approaches were implemented in an existing traffic control system of the B27, a state highway between Stuttgart city, Stuttgart airport, and Tuebingen. The paper compares the results of the new approach with the previous approach based on conventional control technology View full abstract»

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  • A fuzzy decision system for ultrasonic prenatal examination enhancement

    Publication Year: 1996 , Page(s): 1712 - 1717 vol.3
    Cited by:  Papers (1)
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    A fuzzy decision system has been successfully developed to semi-automate noninvasive examination of the fetus. Such a system can be helpful in reducing costs, minimizing exposure of the fetus to ultrasonic radiation, and providing a uniform examination and interpretation of the results. The system relies on pre-processing of the captured image followed by decisional algorithms in the form of a series of If-Then rules. Typical measurements of the head circumference, abdominal circumference, and femur length are used to arrive at appropriate membership functions. The inference system developed identifies almost 96% of the fetal types correctly, indicating that the use of fuzzy inference systems to assist in the analysis of ultrasonic fetal examinations is very promising View full abstract»

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  • Fuzzy sets in distributed traffic control

    Publication Year: 1996 , Page(s): 1617 - 1623 vol.3
    Cited by:  Papers (6)
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    The task of controlling urban traffic requires flexibility, adaptability and handling uncertain information spread through the intersection network. The use of fuzzy sets concepts convey these characteristics to improve system performance. This paper reviews a distributed traffic control system built upon a fuzzy distributed architecture previously developed by the authors. The emphasis of the paper is on the application of the system to control part of Campinas downtown area. Simulation experiments considering several traffic scenarios were performed to verify the capabilities of the system in controlling a set of coupled intersections. The performance of the proposed system is compared with conventional traffic control strategies under the same scenarios. The results obtained show that the distributed traffic control system outperforms conventional systems as far as average queues, average delay and maximum delay measures are concerned View full abstract»

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  • Retrievability and connectedness in fuzzy finite state machines

    Publication Year: 1996 , Page(s): 1586 - 1590 vol.3
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    The predecessor relation on a fuzzy finite state machine is introduced. It is used to derive retrievability and connectedness properties of fuzzy finite state machines View full abstract»

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  • Application of fuzzy control to a road tunnel ventilation system

    Publication Year: 1996 , Page(s): 1736 - 1742 vol.3
    Cited by:  Papers (3)
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    This paper deals with the serious problems of a ventilation system in a large road tunnel. Higher visibility and lower concentration of carbon monoxide are the key issues concerning the ventilation system. Prior to designing the fuzzy control model, a configuration layout of the ventilation system including sensing, control and traffic prediction as well is conceptually constructed. Based on the layout that offers assignments of sensors and control elements, a fuzzy logic control model is developed. Membership functions of sensor errors and control increments are physically submitted in order to set up the fuzzy logic rules. Timing and spacing filtering in terms of weighting approaches is employed in the fuzzy logic rules. A dynamic equation describing the concentration of air pollution is also given so as to cooperate with the fuzzy logic rules and to play roles in the computer simulation. The result of computer simulation reveals that the structure can be an observer to work off line with real road tunnels View full abstract»

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  • Fuzzy sliding control of a force reflecting telerobotic system

    Publication Year: 1996 , Page(s): 2162 - 2167 vol.3
    Cited by:  Papers (1)
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    This paper proposes a new approach to achieve stable force reflecting teleoperation control under time delay-fuzzy sliding control (FSC). FSC is based on the conventional sliding mode control which has been proved robust and stable. The design methodology of FSC includes the following major parts: a fuzzy sliding control law, rule tuning in the phase plane, and soft boundary layer tuning. Stable force reflecting control has been a popular research topic in telerobotics since the 1960s. Because of the stability and robustness of FSC, and the linguistic inference style of fuzzy control, FSC can be easily modified and applied to deal with the uncertainties and human interactions in teleoperation. In our research, a novel control structure which consists of a FSC and a fuzzy supervisor has been implemented in a high bandwidth master-slave telerobotic system. It has been shown that this approach has stable force reflecting and good tracking accuracy for loop delays up to seconds. Experiment results are described in the paper View full abstract»

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  • A design of fuzzy self-organizing controller

    Publication Year: 1996 , Page(s): 1567 - 1572 vol.3
    Cited by:  Papers (1)
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    We propose a fuzzy self-organizing controller which reduces computation time, memory requirements, quantization error, steady-state error and better response is achieved. A software simulation of an inverted pendulum is implemented to verify the method proposed in this paper View full abstract»

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  • Fuzzy logic in digital mammography: analysis of lobulation

    Publication Year: 1996 , Page(s): 1726 - 1731 vol.3
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    This paper illustrates how the fuzzy logic approach can be used to formalize the American College of Radiology (ACR) breast imaging reporting lexicon. In current practice radiologists make a relatively subjective determination for many terms from the lexicon related to breast cancer diagnosis. Lobulation and microlobulation of nodules are important features in breast cancer diagnosis based on mammographic analysis by using the ACR lexicon. We offer an approach for formalizing the distinction of these features and also formalize the description of the intermediate cases between lobulated and microlobulated masses. In this paper it is shown that fuzzy logic can be an effective tool in dealing with this kind of problems. The proposed formalization creates a base for the next two steps: (i) the automatic extraction of the related primitives from the image, and (ii) the detection of lobulated and microlobulated masses based on these primitives View full abstract»

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  • Stability in feedback additive fuzzy systems

    Publication Year: 1996 , Page(s): 1924 - 1930 vol.3
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    Feedback fuzzy systems take their own output as input and define nonlinear rulebased dynamical systems. They use a fixed number of rules to model other dynamical systems while feedforward fuzzy systems suffer from exponential rule explosion. But most feedback fuzzy systems are not themselves stable. Generalized additive fuzzy systems are a special class of feedback fuzzy systems that compute a system output as a convex sum of linear operators. A matrix replaces each then-part fuzzy set function in a standard fuzzy system. The paper proves that continuous versions of these feedback systems are globally asymptotically stable if all rule matrices are stable (negative definite). This does not hold for the better-known discrete version of Tanaka. A corollary shows that it does hold for a special but practical case of the discrete additive model View full abstract»

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  • Refining linear fuzzy rules by reinforcement learning

    Publication Year: 1996 , Page(s): 1750 - 1756 vol.3
    Cited by:  Papers (3)
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    We present an algorithm that refines a set of linear fuzzy rules, which use ellipsoidal radial basis functions in their antecedents and have multiple linear outputs in their consequents (similar to TSK rules), using reinforcement learning. We show how this learning algorithm can be used to refine the performances of controllers for a typical cart-pole balancing system View full abstract»

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  • Text relations and recall based on fuzzy trace theory

    Publication Year: 1996 , Page(s): 1539 - 1545 vol.3
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    Fuzzy trace theory makes a number of claims about the nature of how information is processed, stored and recalled. Brainerd and Reyna (1990) describe fuzzy trace theory as a system in which fuzzy traces serve as the basis of recall and are created by a compound process: raw incoming information is said to undergo a reduction to essential and is encoded for storage based on this processed result, rather than the information in its raw form. In this article, we present a process for transforming texts into quasi mental clusters (QMCs) based on the fuzzy trace theory. The process is interpreted as a particular transformation of a given set of discourse segments and concepts by examining two main types of textual continuity. The model is tested using children's stories and simulation results attest its validity. These clusters are better retained over time and can be regarded as chunks of knowledge extracted from discourses View full abstract»

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