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F.: Building fuzzy graphs: features and taxonomy of learning nongridoriented fuzzy rulebased systems
 International Journal of Intelligent Fuzzy Systems
, 2001
"... The use of linguistic Fuzzy RuleBased Systems (FRBSs) allows us to deal with the modeling of systems building a linguistic model clearly interpretable by human beings. However, the accuracy obtained is not sometimes as good as desired. This fact relates to the restriction imposed when using linguis ..."
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Cited by 8 (4 self)
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The use of linguistic Fuzzy RuleBased Systems (FRBSs) allows us to deal with the modeling of systems building a linguistic model clearly interpretable by human beings. However, the accuracy obtained is not sometimes as good as desired. This fact relates to the restriction imposed when using linguistic variables, which forces the membership functions considered in each fuzzy linguistic rule to belong to a common set of them, i.e., to use a global grid. To solve this problem, in the last few years a new variant has been proposed working directly with fuzzy variables in the fuzzy rules instead of linguistic terms, thus ignoring the said restriction. Therefore, these systems, which are totally equivalent to fuzzy graphs (defined by Zadeh as granular representations of functional dependencies and relations), do not consider a global grid and could be named NonGridOriented (NGO) FRBSs. Of course, the main objective of these models is the accuracy of the system instead its interpretability. Until now, NGO FRBSs have been little considered and developed in the litera
Simplified ART: A new class of ART algorithms
, 1998
"... The Simplified Adaptive Resonance Theory (SART) class of networks is proposed to handle problems encountered in Adaptive Resonance Theory 1 (ART 1)based algorithms when detection of binary and analog patterns is performed. The basic idea of SART is to substitute ART 1based "unidirectional&q ..."
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Cited by 7 (4 self)
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The Simplified Adaptive Resonance Theory (SART) class of networks is proposed to handle problems encountered in Adaptive Resonance Theory 1 (ART 1)based algorithms when detection of binary and analog patterns is performed. The basic idea of SART is to substitute ART 1based "unidirectional" (asymmetric) activation and match functions with "bidirectional" (symmetric) function pairs. This substitution makes the class of SART algorithms potentially more robust and less timeconsuming than ART 1based systems. One SART algorithm, termed Fuzzy SART, is discussed. Fuzzy SART employs probabilistic and possibilistic fuzzy membership functions to combine soft competitive learning with outlier detection. Its soft competitive strategy relates Fuzzy SART to the wellknown SelfOrganizing Map and Neural Gas clustering algorithm. A new Normalized Vector Distance, which can be employed by Fuzzy SART, is also presented. Fuzzy SART performs better than ART 1based CarpenterGrossbergRosen Fuzzy ART ...
Automatic Spectral RuleBased Preliminary Mapping of Calibrated Landsat TM and ETM+ Images
"... Abstract—Based on purely spectraldomain prior knowledge taken from the remote sensing (RS) literature, an original spectral (fuzzy) rulebased perpixel classifier is proposed. Requiring no training and supervision to run, the proposed spectral rulebased system is suitable for the preliminary clas ..."
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Cited by 5 (3 self)
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Abstract—Based on purely spectraldomain prior knowledge taken from the remote sensing (RS) literature, an original spectral (fuzzy) rulebased perpixel classifier is proposed. Requiring no training and supervision to run, the proposed spectral rulebased system is suitable for the preliminary classification (primal sketch, in the Marr sense) of Landsat5 Thematic Mapper and Landsat7 Enhanced Thematic Mapper Plus images calibrated into planetary reflectance (albedo) and atsatellite temperature. The classification system consists of a modular hierarchical topdown processing structure, which is adaptive to image statistics, computationally efficient, and easy to modify, augment, or scale to other sensors’ spectral properties, like those of the Advanced Spaceborne Thermal Emission and Reflection Radiometer and of the Satellite Pour l’Observation de la Terre (SPOT4 and5). As output, the proposed system detects a set of meaningful and reliable fuzzy
Septic Shock Diagnosis by Neural Networks and Rule Based Systems
 in: L.C. Jain: Computational Intelligence Techniques In Medical Diagnosis And Prognosis
, 2001
"... In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies. ..."
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Cited by 4 (4 self)
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In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies.
Interpretability Improvements to Find the Balance InterpretabilityAccuracy in Fuzzy Modeling: An Overview
"... Abstract. System modeling with fuzzy rulebased systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to fa ..."
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Cited by 3 (0 self)
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Abstract. System modeling with fuzzy rulebased systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mainly developed by linguistic FRBSs) is focused on the interpretability, precise FM (mainly developed by TakagiSugenoKang FRBSs) is focused on the accuracy. Since both criteria are of vital importance in system modeling, the balance between them has started to pay attention in the fuzzy community in the last few years. The chapter analyzes mechanisms to find this balance by improving the interpretability in linguistic FM: selecting input variables, reducing the fuzzy rule set, using more descriptive expressions, or performing linguistic approximation; and in precise FM: reducing the fuzzy rule set, reducing the number of fuzzy sets, or exploiting the local description of the rules. 1
Unsupervised Fuzzy Clustering Using Weighted Incremental Neural Networks
 International Journal of Neural Systems (IJNS), World Scientific Publishing Co. Pte Ltd
, 2004
"... A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FCWINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of ..."
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A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FCWINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershedlike procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FCWINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FCWINN algorithm is computationally efficient when compared to other approaches for clustering large highdimensional data sets.
The PNC 2 Cluster Algorithm  An integrated learning algorithm for rule induction
, 2003
"... This document describes the hierarchical agglomerative cluster algorithm Pnc 2 in the context of direct generation of IfThen rules for classification tasks. As an agglomerative cluster algorithm, the Pnc 2 initializes each learn data tuple as a single cluster. Then, if a merge test is passed, itera ..."
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This document describes the hierarchical agglomerative cluster algorithm Pnc 2 in the context of direct generation of IfThen rules for classification tasks. As an agglomerative cluster algorithm, the Pnc 2 initializes each learn data tuple as a single cluster. Then, if a merge test is passed, iteratively always those two clusters with the same output value are merged, that are closest to each other. The merge test transforms the generalized cluster into a rule and evaluates it by a kind of hitrate. The rule's premise is the cuboid, that encloses the input vectors of all learn data tuples merged in the cluster. This representation suffers in high dimensional input spaces due to the COD problem and thus a special mechanism is used to extend the cuboid during the merge test. A
Topology Based Fuzzy Clustering for Robust ANFIS Creation
"... Abstract—This paper describes how the clustering topology of an input space data distribution is utilized to properly initialize an Adaptive NeuroFuzzy Inference System (ANFIS). We used a new unsupervised clustering algorithm called Topology based Fuzzy Clustering (TFC) that performs better than Gr ..."
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Abstract—This paper describes how the clustering topology of an input space data distribution is utilized to properly initialize an Adaptive NeuroFuzzy Inference System (ANFIS). We used a new unsupervised clustering algorithm called Topology based Fuzzy Clustering (TFC) that performs better than Growing Neural Gas (GNG) in extracting the inputspace topology. The topology information in the form of number of nodes, node positions and node connectivity is used for the initialization of the ANFIS. Using two robotic modeling tasks as benchmarks, we demonstrate the improved performance of TFCderived ANFIS when compared to the subclustering method found in the Fuzzy Logic Toolbox of Matlab. I.
Applying RBF Neural Nets for Position Control of an Inter/Scara Robot
"... Abstract: This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback err ..."
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Abstract: This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained online have been used, without requiring any previous knowledge about the system to be controlled. These approach has performed very successfully, with better results obtained with the RBF networks when compared to PID and sliding mode positional controllers.
Scatterpartitioning RBF network for function regression and image segmentation: Preliminary results
, 1998
"... Scatterpartitioning Radial Basis Function (RBF) networks increase their number of degrees of freedom with the complexity of an inputoutput mapping to be estimated on the basis of a supervised training data set. Due to its superior expressive power a scatterpartitioning Gaussian RBF (GRBF) model, ..."
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Scatterpartitioning Radial Basis Function (RBF) networks increase their number of degrees of freedom with the complexity of an inputoutput mapping to be estimated on the basis of a supervised training data set. Due to its superior expressive power a scatterpartitioning Gaussian RBF (GRBF) model, termed Supervised Growing Neural Gas (SGNG), is selected from the literature. SGNG employs a onestage errordriven learning strategy and is capable of generating and removing both hidden units and synaptic connections. A slightly modified SGNG version is tested as a function estimator when the training surface to be fitted is an image, i.e., a 2D signal whose size is finite. The relationship between the generation, by the learning system, of disjointed maps of hidden units and the presence, in the image, of pictorially homogeneous subsets (segments) is investigated. Unfortunately, the examined SGNG version performs poorly both as function estimator and image segmenter. This may be due to...