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Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Date 4-9 May 1998

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  • The 1998 IEEE International Joint Conference on Neural Networks Proceedings [front matter]

    Publication Year: 1998 , Page(s): i - xxxvi
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    Freely Available from IEEE
  • Author's index

    Publication Year: 1998 , Page(s): A
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  • Robot path planning for maze navigation

    Publication Year: 1998 , Page(s): 2081 - 2085 vol.3
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (408 KB)  

    This paper presents the application of multilayer perceptrons to the robot path planning problem, and in particular to the task of maze navigation. Previous published results implied that the training of feedforward multilayered networks failed, because of the nonsmoothness of data. Here the same maze problem is revisited View full abstract»

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  • A robust neural controller for underwater robot manipulator

    Publication Year: 1998 , Page(s): 2098 - 2103 vol.3
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (416 KB)  

    This paper presents a robust control scheme wing a multilayer neural network. The multilayer neural network acts as a compensator of the conventional sliding mode controller to maintain the control performance when the initial assumptions of uncertainty bounds are not valid. The proposed controller applies to control the robot manipulator operating under the sea which has large uncertainties such ... View full abstract»

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  • A general approach for hysteresis modeling and identification using neural networks

    Publication Year: 1998 , Page(s): 2425 - 2428 vol.3
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (316 KB)  

    In this paper, we will present an approach to identify hysteresis using a slope sensitive neural network and a modified Luenberger observer. Identification is based on a general model of hysteresis without the need of internal states. Our identification approach provides mathematically stable adaptation and has shown excellent simulation results for various types of hysteresis View full abstract»

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  • Neural networks for nonlinear mutual prediction of coupled chaotic time series

    Publication Year: 1998 , Page(s): 1937 - 1942 vol.3
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (292 KB)  

    Multilayer perceptrons (MLP) were trained to mutually predict nonlinearly coupled identical Henon systems. Several combinations of input and target time series were presented to networks of different structure during training. After presenting the trained networks with a short segment of Henon data they were able to generate Henon time series of variable duration. This was verified by comparing th... View full abstract»

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  • Optimal tailoring of trajectories, growing training sets and recurrent networks for spoken word recognition

    Publication Year: 1998 , Page(s): 2169 - 2174 vol.3
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    A novel system that efficiently integrates two types of neural networks for reliably performing isolated word recognition is described. The recognition system comprises of a feature extractor that includes a self organizing map for an optimal tailoring of trajectory representations of words in reduced dimension feature spaces. Experimental results indicate that such lower dimensional trajectories ... View full abstract»

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  • A temporal adaptive probability neural network for cloud classification from satellite imagery

    Publication Year: 1998 , Page(s): 1732 - 1737 vol.3
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    Cloud classification from satellite imagery is an important but very difficult task. Temporal changes are one of the main factors that cause degradation in the classifier performance when a sequence of imagery is to be processed A probability neural network (PNN)-based cloud classification system and its temporal updating scheme is proposed in this paper. This novel approach can track the temporal... View full abstract»

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  • A hybrid structure for adaptive fixed weight recurrent networks

    Publication Year: 1998 , Page(s): 1926 - 1931 vol.3
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (388 KB)  

    Due to the evolution of the underlying physical process, a correct model can transform into an erroneous one. We therefore propose a method which overcomes this problem by adapting the network along the way. Our method (clustered error injection) is based (a) on the ability of real-time recurrent learning networks to form clustered network structures and (b) on the error injection principle. The a... View full abstract»

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  • Recurrent RBF networks for suspension system modeling and wear diagnosis of a damper

    Publication Year: 1998 , Page(s): 2441 - 2446 vol.3
    Cited by:  Papers (2)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (452 KB)  

    From the beginning of the nineties, artificial neural networks began to be used for the identification and control of nonlinear systems. Thus, new types of nets appeared permitting to model the behaviour of dynamical systems. These recurrent networks extend the range of engineering applications and emerged as new powerful architectures. Besides, local models like RBFN (or neuro-fuzzy structures) p... View full abstract»

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  • A training data selection in on-line training for multilayer neural networks

    Publication Year: 1998 , Page(s): 2247 - 2252 vol.3
    Cited by:  Papers (3)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (480 KB)  

    In this paper, a training data selection method for multilayer neural networks (MLNNs) in online training is proposed. Purpose of the reduction in training data is reducing the computation complexity of the training and saving the memory to store the data without losing generalization performance. This method uses a pairing method, which selects the nearest neighbor data by finding the nearest dat... View full abstract»

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  • Prediction of stochastic fields by RBFNN

    Publication Year: 1998 , Page(s): 1960 - 1965 vol.3
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (532 KB)  

    A statistical description of stochastic phenomena is utilized to formulate a general modeler of physical laws having the structure of a radial basis function neural network. As a basis for the description of a phenomenon the concept of an auto-regressive field is utilized. Its evolution is represented by a non-linear mapping relation in which the generating function is modeled empirically by a non... View full abstract»

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  • Automatic language identification with recurrent neural networks

    Publication Year: 1998 , Page(s): 2184 - 2189 vol.3
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (616 KB)  

    Automatic language identification (LID), an important domain in speech processing, means the capability of a machine to determine a natural language from a spoken utterance. We present a novel approach to LID, which involves recurrent neural networks (RNN) as the main mechanism. We propose that, because of acoustical context issues, RNNs are particularly suitable for the LID task. Our approach als... View full abstract»

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  • Batch self-organizing maps on a unit sphere

    Publication Year: 1998 , Page(s): 2273 - 2276 vol.3
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    Kohonen's batch map over data on a unit sphere is modified. An energy function is proposed and the convergence of the algorithm is proven. It is shown that this deterministic, batch-mode self-organizing algorithm is efficient and performs well View full abstract»

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  • Neural network recognition and analysis of hand-printed characters

    Publication Year: 1998 , Page(s): 1743 - 1747 vol.3
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (428 KB)  

    The main objective of this paper is to introduce a novel method of feature extraction for character data and develop a neural network system for recognising different Latin characters. In this paper we describe feature extraction, neural network development for character recognition and perform further neural network analysis on noisy image segments to explain the qualitative and quantitative aspe... View full abstract»

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  • Phased backpropagation: a hybrid of BPTT and temporal BP

    Publication Year: 1998 , Page(s): 2262 - 2267 vol.3
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (420 KB)  

    We present a synthesis of backpropagation through time (BPTT) and temporal backpropagation (TB) that permits the efficiency of TB in dealing with delay lines to be combined with the generality of BPTT with respect to arbitrary discrete-time network structures. We express our formulation in terms of an ordered network, subsuming less general network architectures such as time-delay networks and rec... View full abstract»

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  • Modeling time dependencies in the mixture of experts

    Publication Year: 1998 , Page(s): 2324 - 2327 vol.3
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (324 KB)  

    The mixture of experts, as it was originally formulated, is a static algorithm in the sense that the output of the network, and parameter updates during training, are completely independent from one time step to the next. This independence creates difficulties when the model is applied to time series prediction. We address this by adding memory to the mixture of experts. A Gaussian assumption on e... View full abstract»

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  • A case study on bagging, boosting and basic ensembles of neural networks for OCR

    Publication Year: 1998 , Page(s): 1828 - 1833 vol.3
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (500 KB)  

    We study the effectiveness of three neural network ensembles in improving OCR performance: basic, bagging, and boosting. Three random character degradation models are introduced for training individual networks in order to reduce error correlation between individual network and to improve the generalization ability of neural networks. We compare the recognition accuracies of these three ensembles ... View full abstract»

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  • Hierarchical radial basis function networks

    Publication Year: 1998 , Page(s): 1893 - 1898 vol.3
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (476 KB)  

    Ersoy (1991) and Ersoy and Hong (1990) have constructed a neural network architecture called the parallel, self-organizing, hierarchical neural network (PSHNN) that contains a number of stage neural networks. In their papers, the stage networks are one-layer networks with delta rule learning. They report the result by using PSHNN in solving some classification problems, but how effective it is com... View full abstract»

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  • A neural field approach to topological reinforcement learning in continuous action spaces

    Publication Year: 1998 , Page(s): 1992 - 1997 vol.3
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (788 KB)  

    We present a neural field approach to distributed Q-learning in continuous state and action spaces that is based on action coding and selection in dynamic neural fields. It is, to the best of our knowledge, one of the first attempts that combines the advantages of a topological action coding with a distributed action-value learning in one neural architecture. This combination, supplemented by a ne... View full abstract»

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  • A methodology for information theoretic feature extraction

    Publication Year: 1998 , Page(s): 1712 - 1716 vol.3
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (404 KB)  

    We discuss an unsupervised feature extraction method which is driven by an information theoretic based criterion: mutual information. While information theoretic signal processing has been examined by many authors the method presented here is more closely related to the approaches of Linsker (1988, 1990), Bell and Sejnowski (1995), and Viola et al. (1996). The method we discuss differs from previo... View full abstract»

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  • A data approach alternative at system identification and modeling using the self-organizing associative memory (SAM) system

    Publication Year: 1998 , Page(s): 2447 - 2452 vol.3
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (512 KB)  

    We introduce a data-based approach alternative to the rule-based parameter approach toward system identification. Motivated by the design-intensive problem of the parameter approach, the self-organizing associative memory (SAM) system seeks to represent the system using a subset of stored training data. We surmise that knowledge is association between memorized objects, not memorized rules. We pos... View full abstract»

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  • Incorporation of statistical methods in multi-step neural network prediction models

    Publication Year: 1998 , Page(s): 2513 - 2518 vol.3
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (652 KB)  

    This paper addresses the problems associated with multistep ahead prediction neural networks models. We will see how some concepts from the statistical theory field can be applied in various ways to improve the modelling. The generalization and error autocorrelation problems will he addressed using topological and methodological approach among which network committees, statistical bootstrap and pr... View full abstract»

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  • Neural network based solution to inverse problems

    Publication Year: 1998 , Page(s): 2471 - 2476 vol.3
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (460 KB)  

    The well-posedness of problems is not always guaranteed in inverse problems, unlike in forward problems. Thus, a number of methods for giving well-posedness have been studied in mathematical fields. In the field of neural networks, the network inversion method for solving inverse problems was proposed; it is useful but does not remove the ill-posedness of inverse problems. To overcome the difficul... View full abstract»

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  • Ship's classification by its magnetic signature

    Publication Year: 1998 , Page(s): 1889 - 1892 vol.3
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (208 KB)  

    A ship's classification by its magnetic signatures is of great importance in the development of magnetic mines. This work concerns the use of a neural network classification system combined with the relevant features method to solve this problem View full abstract»

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