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Neural Networks, IEEE Transactions on

Issue 5 • Date Sep 1992

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Displaying Results 1 - 18 of 18
  • Multilayer perceptron, fuzzy sets, and classification

    Publication Year: 1992 , Page(s): 683 - 697
    Cited by:  Papers (121)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1324 KB)  

    A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and other related models View full abstract»

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  • Fuzzy neural networks with reference neurons as pattern classifiers

    Publication Year: 1992 , Page(s): 770 - 775
    Cited by:  Papers (23)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (384 KB)  

    A heterogeneous neural network consisting of logic neurons and realizing mappings in [0, 1] hypercubes is presented. The two kinds of neurons studied are utilized to perform matching functions (equality or reference neurons) and aggregation operations (aggregation neurons). All computations are driven by logic operations widely used in fuzzy set theory. The network is heterogeneous in its nature and includes two types of neurons organized into a structure detecting individual regions of patterns (using reference neurons) and combining them to yield a final classification decision View full abstract»

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  • Fuzzy basis functions, universal approximation, and orthogonal least-squares learning

    Publication Year: 1992 , Page(s): 807 - 814
    Cited by:  Papers (618)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB)  

    Fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions. Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, an orthogonal least-squares (OLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs; then, the OLS algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system. The fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system, and the simulation results show that the control performance is improved by incorporating some common-sense fuzzy control rules View full abstract»

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  • A neural net for extracting knowledge from natural language data bases

    Publication Year: 1992 , Page(s): 819 - 828
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (900 KB)  

    A model of a fuzzy neuron, one which increases the computational power of the artificial neuron, turning it also into a symbolic processing device, is presented. The model proposes the synapsis to be symbolically and numerically defined, by means of the assignment of tokens to the presynaptic and postsynaptic neurons. The matching or concatenation compatibility between these tokens is used to decide about the possible connections among neurons of a given net. The strength of the compatible synapsis is made dependent on the amount of the available presynaptic and postsynaptic tokens. The symbolic and numeric processing capacity of the new fuzzy neuron is used to build a neural net (JARGON) to disclose the existing knowledge in natural language databases such as medical files, sets of interviews and reports about engineering operations View full abstract»

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  • A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

    Publication Year: 1992 , Page(s): 672 - 682
    Cited by:  Papers (119)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1128 KB)  

    Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared View full abstract»

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  • Numerical convergence and interpretation of the fuzzy c-shells clustering algorithm

    Publication Year: 1992 , Page(s): 787 - 793
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB)  

    R. N. Dave's (1990) version of fuzzy c-shells is an iterative clustering algorithm which requires the application of Newton's method or a similar general optimization technique at each half step in any sequence of iterates for minimizing the associated objective function. An important computational question concerns the accuracy of the solution required at each half step within the overall iteration. The general convergence theory for grouped coordination minimization is applied to this question to show that numerically exact solution of the half-step subproblems in Dave's algorithm is not necessary. One iteration of Newton's method in each coordinate minimization half step yields a sequence obtained using the fuzzy c-shells algorithm with numerically exact coordinate minimization at each half step. It is shown that fuzzy c-shells generates hyperspherical prototypes to the clusters it finds for certain special cases of the measure of dissimilarity used View full abstract»

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  • Learning and tuning fuzzy logic controllers through reinforcements

    Publication Year: 1992 , Page(s): 724 - 740
    Cited by:  Papers (263)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1168 KB)  

    A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing View full abstract»

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  • Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps

    Publication Year: 1992 , Page(s): 698 - 713
    Cited by:  Papers (401)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1608 KB)  

    A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a piecewise-continuous function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system View full abstract»

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  • Adaptive fuzzy leader clustering of complex data sets in pattern recognition

    Publication Year: 1992 , Page(s): 794 - 800
    Cited by:  Papers (31)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB)  

    A modular, unsupervised neural network architecture that can be used for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns online in a stable and efficient manner. The system used a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid position from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The AFLC algorithm is applied to the Anderson iris data and laser-luminescent finger image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets View full abstract»

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  • Evidence aggregation networks for fuzzy logic inference

    Publication Year: 1992 , Page(s): 761 - 769
    Cited by:  Papers (29)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (768 KB)  

    Fuzzy logic has been applied in many engineering disciplines. The problem of fuzzy logic inference is investigated as a question of aggregation of evidence. A fixed network architecture employing general fuzzy unions and intersections is proposed as a mechanism to implement fuzzy logic inference. It is shown that these networks possess desirable theoretical properties. Networks based on parameterized families of operators (such as Yager's union and intersection) have extra predictable properties and admit a training algorithm which produces sharper inference results than were earlier obtained. Simulation studies corroborate the theoretical properties View full abstract»

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  • The fuzzy c spherical shells algorithm: A new approach

    Publication Year: 1992 , Page(s): 663 - 671
    Cited by:  Papers (56)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (776 KB)  

    The fuzzy c spherical shells (FCSS) algorithm is specially designed to search for clusters that can be described by circular arcs or, generally, by shells of hyperspheres. A new approach to the FCSS algorithm is presented. This algorithm is computationally and implementationally simpler than other clustering algorithms that have been suggested for this purpose. An unsupervised algorithm which automatically finds the optimum number of clusters is not known. It uses a cluster validity measure to identify good clusters, merges all compatible clusters, and eliminates spurious clusters to achieve the final results. Experimental results on several data sets are presented View full abstract»

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  • Neural fuzzy logic programming

    Publication Year: 1992 , Page(s): 815 - 818
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB)  

    A foundational development of propositional fuzzy logic programs is presented. Fuzzy logic programs are structured knowledge bases including uncertainties in rules and facts. The precise specifications of uncertainties have a great influence on the performance of the knowledge base. It is shown how fuzzy logic programs can be transformed to neural networks, where adaptations of uncertainties in the knowledge base increase the reliability of the program and are carried out automatically View full abstract»

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  • Self-learning fuzzy controllers based on temporal backpropagation

    Publication Year: 1992 , Page(s): 714 - 723
    Cited by:  Papers (226)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB)  

    A generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner is presented. This methodology, termed temporal backpropagation, is model-sensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules of human experts or automatically derive the fuzzy if-then rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller View full abstract»

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  • Adaptive fuzzy c-shells clustering and detection of ellipses

    Publication Year: 1992 , Page(s): 643 - 662
    Cited by:  Papers (60)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1460 KB)  

    Several generalizations of the fuzzy c-shells (FCS) algorithm are presented for characterizing and detecting clusters that are hyperellipsoidal shells. An earlier generalization, the adaptive fuzzy c-shells (AFCS) algorithm, is examined in detail and is found to have global convergence problems when the shapes to be detected are partial. New formulations are considered wherein the norm inducing matrix in the distance metric is unconstrained in contrast to the AFCS algorithm. The resulting algorithm, called the AFCS-U algorithm, performs better for partial shapes. Another formulation based on the second-order quadrics equation is considered. These algorithms can detect ellipses and circles in 2D data. They are compared with the Hough transform (HT)-based methods for ellipse detection. Existing HT-based methods for ellipse detection are evaluated, and a multistage method incorporating the good features of all the methods is used for comparison. Numerical examples of real image data show that the AFCS algorithm requires less memory than the HT-based methods, and it is at least an order of magnitude faster than the HT approach View full abstract»

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  • Fuzzy min-max neural networks. I. Classification

    Publication Year: 1992 , Page(s): 776 - 786
    Cited by:  Papers (204)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1048 KB)  

    A supervised learning neural network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregate (union) of fuzzy set hyperboxes. A fuzzy set hyperbox is an n-dimensional box defined by a min point and a max point with a corresponding membership function. The min-max points are determined using the fuzzy min-max learning algorithm, an expansion-contraction process that can learn nonlinear class boundaries in a single pass through the data and provides the ability to incorporate new and refine existing classes without retraining. The use of a fuzzy set approach to pattern classification inherently provides a degree of membership information that is extremely useful in higher-level decision making. The relationship between fuzzy sets and pattern classification is described. The fuzzy min-max classifier neural network implementation is explained, the learning and recall algorithms are outlined, and several examples of operation demonstrate the strong qualities of this new neural network classifier View full abstract»

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  • Neural networks designed on approximate reasoning architecture and their applications

    Publication Year: 1992 , Page(s): 752 - 760
    Cited by:  Papers (46)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (876 KB)  

    The NARA (neural networks based on approximate reasoning architecture) model is proposed and its composition procedure and evaluation are described. NARA is a neural network (NN) based on the structure of fuzzy inference rules. The distinctive feature of NARA is that its internal state can be analyzed according to the rule structure, and the problematic portion can be easily located and improved. The ease with which performance can be improved is shown by applying the NARA model to pattern classification problems. The NARA model is shown to be more efficient than ordinary NN models. In NARA, characteristics of the application task can be built into the NN model in advance by employing the logic structure, in the form of fuzzy inference rules. Therefore, it is easier to improve the performance of NARA, in which the internal state can be observed because of its structure, than that of an ordinary NN model, which is like a black box. Examples are introduced by applying the NARA model to the problems of auto adjustment of VTR tape running mechanisms and alphanumeric character recognition View full abstract»

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  • A connectionist approach for rule-based inference using an improved relaxation method

    Publication Year: 1992 , Page(s): 741 - 751
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (988 KB)  

    A connectionist mechanism for an inference problem alternative to the usual chaining method is described. The inference problem is within the scope of propositional logic that contains no variables and with some enhanced knowledge representation facilities. The method is an application of mathematical programming where knowledge and data are transformed into constraint equations. In the network, the nodes represent propositions and constraint equations, and the violation of constraints is formulated as an energy function. The inference is realized as a minimization process of the energy function using the relaxation method to search for a truth value distribution that achieves the optimum consistency with the given knowledge and data. A modified relaxation method is proposed to improve the computational inefficiencies associated with the optimization process. The behavior of the method is analyzed through examples of deductive and abductive inference and of inference with unorganized knowledge View full abstract»

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  • On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm

    Publication Year: 1992 , Page(s): 801 - 806
    Cited by:  Papers (246)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB)  

    A fuzzy modeling method using fuzzy neural networks with the backpropagation algorithm is presented. The method can identify the fuzzy model of a nonlinear system automatically. The feasibility of the method is examined using simple numerical data View full abstract»

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Aims & Scope

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.

 

This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.

Full Aims & Scope