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Fuzzy Systems, 2006 IEEE International Conference on

Date 16-21 July 2006

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Displaying Results 1 - 25 of 353
  • Table of contents

    Publication Year: 2006 , Page(s): 0_1 - 0_23
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  • An Implication in Fuzzy Sets

    Publication Year: 2006 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB) |  | HTML iconHTML  

    In this paper we introduce a fuzzy implication. The proposed fuzzy implication does not belong in one of the well known three general classes of fuzzy implications (S-implications, R-implications and QL-implications). Also we give an extended model of this fuzzy implication in intuitionistic fuzzy set and/or interval-valued fuzzy sets. View full abstract»

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  • Learning In Lattice Neural Networks that Employ Dendritic Computing

    Publication Year: 2006 , Page(s): 7 - 13
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (622 KB) |  | HTML iconHTML  

    Recent discoveries in neuroscience imply that the basic computational elements are the dendrites that make up more than 50% of a cortical neuron's membrane. Neuroscientists now believe that the basic computation units are dendrites, capable of computing simple logic functions. This paper discusses two types of neural networks that take advantage of these new discoveries. The focus of this paper is on some learning algorithms in the two neural networks. Learning is in terms of lattice computations that take place in the dendritic structure as well as in the cell body of the neurons used in this model. View full abstract»

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  • Default Values to Handle Incomplete Fuzzy Information

    Publication Year: 2006 , Page(s): 14 - 21
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (292 KB) |  | HTML iconHTML  

    Incomplete information is a problem in many aspects of actual environments. Furthermore, in many sceneries the knowledge is not represented in a crisp way. It is common to find fuzzy concepts or problems with some level of uncertainty. There are not many practical systems which handle fuzziness and uncertainty and the few examples that we can find are used by a minority. To extend a popular system (which many programmers are using) with the ability of combining crisp and fuzzy knowledge representations seems to be an interesting issue. Our first work (fuzzy prolog) was a language that models B([0, 1])-valued fuzzy logic. In the Borel algebra, B([0,1]), truth value is represented using unions of intervals of real numbers. This work was more general in truth value representation and propagation than previous works. An interpreter for this language using constraint logic programming over real numbers (CLP(R)) was implemented and is available in the Ciao system . Now, we enhance our former approach by using default knowledge to represent incomplete information in logic programming. We also provide the implementation of this new framework. This new release of fuzzy prolog handles incomplete information, it has a complete semantics (the previous one was incomplete as prolog) and moreover it is able to combine crisp and fuzzy logic in prolog programs. Therefore, new fuzzy prolog is more expressive to represent real world. Fuzzy prolog inherited from prolog its incompleteness. The incorporation of default reasoning to fuzzy prolog removes this problem and requires a richer semantics which we discuss. View full abstract»

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  • T-Norms on Bounded Lattices: t-norm Morphisms and Operators

    Publication Year: 2006 , Page(s): 22 - 28
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (206 KB) |  | HTML iconHTML  

    Triangular norms or t-norm, in short, and automorphisms are very useful to fuzzy logics in the narrow sense. Moreover, these notions are usually limited to the set [0,1]. In this paper we will consider a well known generalization of the t-norm for arbitrary bounded lattices and provide a generalization of automorphism notion for this same structure. We consider several typical lattice constructors and introduce versions of them for t-norms and morphisms. We also analyze some properties of these constructions. View full abstract»

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  • Air Quality Assessment Using Fuzzy Lattice Reasoning (FLR)

    Publication Year: 2006 , Page(s): 29 - 34
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (319 KB) |  | HTML iconHTML  

    Accurate and on-line decision-making is required by decision support systems including those ones used for environmental information management. This paper focuses on air quality assessment and demonstrates the added value of applying data mining techniques in operational decision-making. More specifically, the application of fuzzy lattice reasoning (FLR) classifier is investigated. An enhanced FLR learning algorithm is presented that employs a sigmoid valuation function for introducing tunable non-linearities. The FLR classifier is applied here beyond the unit-hypercube. The FLR with a sigmoid positive valuation function demonstrates an improved performance on a dataset from the region of Valencia, Spain regarding an environmental problem. Descriptive decision making knowledge (i.e. rules) for classification is also induced. View full abstract»

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  • Intuitionistic Fuzzy Set Functions, Mass Assignment Theory, Possibility Theory and Histograms

    Publication Year: 2006 , Page(s): 35 - 41
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (177 KB) |  | HTML iconHTML  

    The idea of describing imprecise information taking independently into account both positive and negative aspects lies in the core of intuitionistic fuzzy sets which are generalization of fuzzy sets. But to apply intuitionistic fuzzy sets which seem to be a very good tool for representation and processing of imperfect information, one should be able to assign their membership and non-membership functions. In this paper we propose two ways of assigning the membership and non-membership functions for intuitionistic fuzzy sets: a) -by asking experts; b) -from relative frequency distributions (histograms). To justify the second (automatic) method, we show some similarities/parallels between intuitionistic fuzzy set theory and mass assignment theory -a well known tool for dealing with both probabilistic and fuzzy uncertainties. We also recall a semantic for membership functions -the interpretation having its roots in the possibility theory. Finally, we propose the automatic algorithm assigning the functions describing intuitionistic fuzzy sets. View full abstract»

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  • A New Cluster Validity Index for Fuzzy Clustering based on Combination of Dual Triples

    Publication Year: 2006 , Page(s): 42 - 47
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2289 KB) |  | HTML iconHTML  

    Cluster validity indexes aim at evaluating the degree to which a partition obtained from a clustering algorithm approximates the real structure of a data set. Most of them reduce to the search of the right number of clusters. This paper presents such a new validity index for fuzzy clustering based on the aggregation of the resulting membership degrees with no additional information, e.g. lite geometrical structure of the data. It exploits the tendency for a data point to belong to a unique cluster, i.e. both the tendency to belong to one cluster and the tendency not to belong to the others clusters. View full abstract»

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  • Finding the Number of Fuzzy Clusters by Resampling

    Publication Year: 2006 , Page(s): 48 - 54
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (289 KB) |  | HTML iconHTML  

    Recently several papers studied resampling approaches to determine the number of clusters in prototype-based clustering. The core idea underlying these approaches is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster structures. In this paper we investigate whether these approaches can be transferred to fuzzy clustering. It turns out that they are applicable to fuzzy clustering as well, but that not all relative cluster evaluation measures that work for crisp clustering can also be used for fuzzy clustering. View full abstract»

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  • Fuzzy Clustering Algorithms for Symbolic Interval Data based on L2 Norm

    Publication Year: 2006 , Page(s): 55 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (335 KB) |  | HTML iconHTML  

    The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper introduces fuzzy clustering algorithms to partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and a prototype (a vector of intervals) for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare symbolic interval data, the methods use a suitable (adaptive and non-adaptive) L2 norm defined on vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method. View full abstract»

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  • Kernel Based Fuzzy Ant Clustering with Partition Validity

    Publication Year: 2006 , Page(s): 61 - 65
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (269 KB) |  | HTML iconHTML  

    We introduce a new swarm intelligence based algorithm for data clustering with a kernel-induced distance metric. Previously a swarm based approach using artificial ants to optimize the fuzzy c-means (FCM) criterion using the Euclidean distance was developed. However, FCM is not suitable for clusters which are not hyper-spherical and FCM requires the number of cluster centers be known in advance. The swarm based algorithm determines the number of cluster centers of the input data by using a modification to the fuzzy cluster validity metric proposed by Xie and Beni. The partition validity metric was developed based on the kernelized distance measure. Experiments were done with three data sets; the Iris data, an artificially generated data set and a Magnetic Resonance brain image data set. The results show how effectively the kernelized version of validity metric with a fuzzy ant method finds the number of clusters in the data and that it can be used to partition the data. View full abstract»

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  • Rule Visualization based on Multi-Dimensional Scaling

    Publication Year: 2006 , Page(s): 66 - 71
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (357 KB) |  | HTML iconHTML  

    This paper presents an approach to visualizing and exploring high-dimensional rules in two-dimensional views. The proposed method uses multi-dimensional scaling to place the rule centers and subsequently extends the rules' regions to depict their overlap. This results not only in a visualization of the rules' distribution but also enables the relationship to their immediate neighbors to be judged. The proposed technique is illustrated and discussed on a number of well-known benchmark data sets. View full abstract»

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  • Unsupervised Image Segmentation and Annotation for Content-Based Image Retrieval

    Publication Year: 2006 , Page(s): 72 - 77
    Cited by:  Papers (6)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2029 KB) |  | HTML iconHTML  

    We propose an unsupervised approach to segment color images and annotate its regions. The annotation process uses a multi-modal thesaurus that is built from a large collection of training images by learning associations between low-level visual features and keywords. Association rules are learned through fuzzy clustering and unsupervised feature selection. We assume that a collection of images is available and that each image is globally annotated. The objective is to extract representative visual profiles that correspond to frequent homogeneous regions, and to associate them with keywords. Our approach has three main steps. First, each image is coarsely segmented into regions, and visual features are extracted from each region. Second, the regions are categorized using a fuzzy algorithm that performs clustering and feature weighting simultaneously. As a result, we obtain clusters of regions that share subsets of relevant features. Representatives from each cluster and their relevant visual and textual features would be used to build a thesaurus. Third, fuzzy membership functions are used to label new regions based on their proximity to the thesaurus entries. The proposed approach is trained with a collection of 2,695 images and tested with several different images. View full abstract»

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  • Application of Competitive Clustering to Acquisition of Human Manipulation Skills Acquisition

    Publication Year: 2006 , Page(s): 78 - 83
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (413 KB) |  | HTML iconHTML  

    The work carried out to explore the feasibility of reconstructing human constrained motion manipulation skills is reported. This is achieved by tracing and learning the manipulation performed by a human operator in a haptic rendered virtual environment The peg-in-hole insertion problem is used as a case study. In the developed system, position and contact force and torque as well as orientation data generated in the haptic rendered virtual environment combined with a priori knowledge about the task are used to identify and learn the skills in the newly demonstrated task. The data obtained from the virtual environment is classified into different cluster sets using a competitive fuzzy clustering algorithm called Competitive Agglomeration (CA). The CA algorithm starts with an over specified number of clusters which compete for feature points in the training procedure. Clusters with small cardinalities lose the competition and gradually vanish. The optimal number of clusters that win the competition is eventually determined. The clusters in the optimum cluster set are tuned using Locally Weighted Regression (LWR) to produce prediction models for robot trajectory performing the physical assembly based on the force/position information received from the rig. A background on the work and its significance is provided. The approach developed is explained and the results obtained so far are presented. View full abstract»

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  • Design of Fuzzy Regulators with Optimal Initial Conditions Compensation

    Publication Year: 2006 , Page(s): 84 - 91
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (388 KB) |  | HTML iconHTML  

    In almost all cases, the goal of the design of automatic control systems is to obtain the parameters of the controllers, which are described by differential equations. In general, the controller is artificially built and it is possible to update its initial conditions. In the design of optimal quadratic regulators, the initial conditions of the controller can be changed in an optimal way and they can improve the performance of the controlled system. Following this idea, a LMI-based design procedure to update the initial conditions of PI controllers, considering the nonlinear plant described by Takagi-Sugeno fuzzy models, is presented. The importance of the proposed method is that it also allows other specifications, such as, the decay rate and constraints on control input and output. The application in the control of an inverted pendulum illustrates the effectively of proposed method. View full abstract»

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  • Learning for Hierarchical Fuzzy Systems Based on the Gradient-Descent Method

    Publication Year: 2006 , Page(s): 92 - 99
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (369 KB) |  | HTML iconHTML  

    Standard fuzzy systems suffer the "curse of dimensionality" which has become the bottleneck when applying fuzzy systems to solve complex and high dimensional application problems. This curse of dimensionality results in a larger number of fuzzy rules which reduces the transparency of fuzzy systems. Furthermore too many rules also reduce the generalization capability of fuzzy systems. Hierarchical fuzzy systems have emerged as an effective alternative to overcome this curse of dimensionality and have attracted much attention. However, research on learning methods for hierarchical fuzzy systems and applications is rare. In this paper, we propose a scheme to construct general hierarchical fuzzy systems based on the gradient-descent method. To show the advantages of the proposed method (in terms of accuracy, transparency, generalization capability and fewer rules), this method is applied to a function approximation problem and the result is compared with those obtained by standard (flat) fuzzy systems. View full abstract»

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  • A Novel Reinforcement Learning Approach for Automatic Generation of Fuzzy Inference Systems

    Publication Year: 2006 , Page(s): 100 - 105
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (292 KB) |  | HTML iconHTML  

    In this paper, a novel approach termed dynamic self-generated fuzzy Q-learning (DSGFQL) for automatically generating fuzzy inference systems (FISs) is presented. The DSGFQL methodology can automatically create, delete and adjust fuzzy rules without any priori knowledge. Compared with conventional fuzzy Q-learning (FQL) approaches which only use reinforcement learning (RL) for the consequents part of an FIS, the most salient feature of the DSGFQL is that it applies RL to generate both preconditioning and consequent parts of the FIS. The preconditioning parts of the FIS are formed by RL approaches as well as the epsiv-completeness criteria. On the other hand, the consequent parts of the FIS are updated by FQL. Compared with our previously proposed generalized dynamic fuzzy neural networks (GDFNN), which is a Supervised Learning (SL) approach, the DSGFQL approach can be applied to situations when the training teacher is not available. Compared with the previously proposed dynamic fuzzy Q-learning (DFQL) and online clustering and Q-value based genetic algorithm learning schemes for fuzzy system design (CQGAF), the DSGFQL approach can delete unsatisfactory and redundant fuzzy rules as well as adjust the membership of fuzzy functions. Simulation studies on a wall-following task by a mobile robot show that the proposed DSGFQL algorithm is superior to DFQL and CQGAF. View full abstract»

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  • Can Artificial Life Emerge in a Network of Interacting Agents?

    Publication Year: 2006 , Page(s): 106 - 113
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (389 KB) |  | HTML iconHTML  

    An interacting multi-agent system in a network can behave like a nature-inspired smart system (SS) exhibiting the four salient properties of an artificial life system (ALS): (i) Collective, coordinated and efficient (ii) Self-organization and emergence (iii) Power law scaling or scale invariance under emergence (iv) Adaptive, fault tolerant and resilient against damage. We explain how these basic properties can arise among agents through random enabling, inhibiting, preferential attachment and growth of a multiagent system. However,the quantitative understanding of a Smart system with an arbitrary interactive topology is extremely difficult. Hence we cannot design a general purpose programmable Smart system. However, for specific applications and a predefined static interactive topology among the agents, the quantitative parameters can be obtained through simulation to build a specific SS. View full abstract»

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  • A Goal-oriented Approach to Goal Selection and Action Selection

    Publication Year: 2006 , Page(s): 114 - 121
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (194 KB) |  | HTML iconHTML  

    Goal selection and action selection are important topics in agent research. This paper first describes an agent model, Goal Net, which supports multiple goal selection methods and action selection mechanisms for an agent. Then a goal selection algorithm and action selection mechanisms supported by Goal Net are presented. Examples from real system development are also given to illustrate the algorithm and mechanisms. View full abstract»

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  • The Role of Spiking Neurons for Visual Perception of a Partner Robot

    Publication Year: 2006 , Page(s): 122 - 129
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (935 KB) |  | HTML iconHTML  

    This paper discusses the visual perception for natural communication between a partner robot and a human. The prediction is very important to reduce the computational cost and to extract the perceptual information for the natural communication with a human in the future. Therefore we propose a prediction-based control of visual perception based on spiking neurons. The proposed method is composed of four layers: the input layer, clustering layer, prediction layer, and perceptual module selection layer. Next, we propose a competitive learning method to perform the clustering of human behavior patterns. Furthermore, the robot select perceptual modules used in the next perception according to the predicted perceptual mode. The results of prediction are evaluated based on the Gaussian membership function. Furthermore, we show experimental results of the communication between a partner robot and a human based on our proposal method. View full abstract»

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  • Cross-reference Detection Algorithm for the Real Surveillance Systems Based-on Fuzzy Corresponding Map

    Publication Year: 2006 , Page(s): 130 - 136
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (800 KB) |  | HTML iconHTML  

    A detection algorithm with color information for two dynamic images in a real surveillance system is proposed. It considers input region of frame as a detected region or the region behind other objects based on fuzzy corresponding map which describes common regions between input two dynamic images. Detection experimental results for the real surveillance situation show that the proposed algorithm improves 30% of accuracy compared with the independent detection algorithm. The algorithm will be installed in a basis unit for the real surveillance systems. View full abstract»

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  • Reinforcement Learning of Agent with a Staged View in Distance and Direction for the Pursuit Problem

    Publication Year: 2006 , Page(s): 137 - 143
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (301 KB) |  | HTML iconHTML  

    An autonomous agent had a ranged view of the absolute coordinate system, where it can receive accurate information in a range but noting out of the range. This is a considerably artificial situation. In this paper, we propose a staged view in distance and direction of the relative coordinate system, where an agent receives accurate information in neighborhood but only rough information in short and middle-distance areas. It reflects a human's view that we can see easily an object in the neighborhood but more difficult as distance becomes larger and we can see easily an object in the center direction but more difficult in the righter and lefter directions. We show by a numerical experiment for the pursuit problem, a multi-agent's benchmark problem, that the agent with the staged view learns effectively using Q-learning. View full abstract»

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  • Learning by Switching Knowledge Representations-Limiting the Number of Stored Data

    Publication Year: 2006 , Page(s): 144 - 151
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (398 KB) |  | HTML iconHTML  

    When we solve a problem, we initially have no knowledge and we memorize the raw data with observing data. Finally we have general knowledge for solving the problem. To simulate this learning process, we proposed a learning method with switching different levels of knowledge representations, reconstructing knowledge and switching reasoning methods. In the system, all given data are stored to generate new knowledge, but it is different from the one of our human's knowledge acquisition, in which we just memorize a limit number of data. Therefore, we limit it and when the number of stored data exceeds specified size, the system throws away the oldest data. In the simulation, we apply the method to the data set whose classes are changed periodically, and get a better result than the old method. View full abstract»

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  • Fuzzy Ranking of Financial Statements for Fraud Detection

    Publication Year: 2006 , Page(s): 152 - 158
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (291 KB) |  | HTML iconHTML  

    Automatic detection of anomalies in financial statements can decrease the risk of exposure to fraudulent corporate behavior. This paper proposes a method to convert fraud classification rules learned from a genetic algorithm to a fuzzy score representing the degree to which a company's financial statements match those rules. Applying the method to financial data in real time can lead to the early detection of potentially fraudulent corporate behavior. View full abstract»

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  • Credibility Based Fuzzy Portfolio Selection

    Publication Year: 2006 , Page(s): 159 - 163
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (190 KB) |  | HTML iconHTML  

    This paper discusses portfolio selection problem with fuzzy returns. Different from other researches in fuzzy portfolio selection, this paper selects portfolio based on credibility measure instead of possibility measure. In addition, different from Markowitz's mean-variance modelling idea, this paper regards a portfolio with a relatively high variance as safe if its expected value is sufficiently high. One new fuzzy optimization model is provided, and a hybrid intelligent algorithm is presented to solve the model problem in general cases. One numerical example is also given for the sake of illustration. View full abstract»

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