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Neural Networks, 1999. IJCNN '99. International Joint Conference on

10-16 July 1999

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Displaying Results 1 - 25 of 147
  • IJCNN '99 [front matter]

    Publication Year: 1999, Page(s):i - iv
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  • Cellular neural networks for information storage and retrieval: a new design method

    Publication Year: 1999, Page(s):3754 - 3757 vol.6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (239 KB)

    In this paper a new approach to information storage and retrieval using cellular neural networks is developed. The objective is achieved by considering a suitable discrete-time model of these networks and by designing them so that the input information are fed via external inputs rather than initial conditions. The technique, which exploits globally asymptotically stable networks, leads to a facil... View full abstract»

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  • Author index

    Publication Year: 1999, Page(s):4441 - 4452
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    Freely Available from IEEE
  • Application of self-organizing network and MLP for fuzzy rule extraction

    Publication Year: 1999, Page(s):4289 - 4293 vol.6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB)

    A multi-stage algorithm for classification and fuzzy rule extraction from data based on a self-organizing network and a two-layer perceptron network is proposed. Self-organizing techniques are applied to find data prototypes, which are subsequently used to initialize the perceptron network and to produce membership functions from the learned mapping. Fuzzy rules created by the algorithm provide li... View full abstract»

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  • Improving generalisation using neural bidirectional convergence

    Publication Year: 1999, Page(s):4119 - 4124 vol.6
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    This paper considers the performance of cross-validation across runs in terms of efficiency and accuracy and a method for improving it. A heuristic method loosely inspired by Mitchell's concept and version spaces technique is proposed for recognising when and to what extent the learning runs obtain an optimal generalisation performance. The approach used, the neural bidirectional convergence (NBDC... View full abstract»

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  • Using adaptive resonance theory networks and fuzzy matching to recognize target features in thermal images

    Publication Year: 1999, Page(s):4284 - 4288 vol.6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    Allocating resources to targets in a military engagement is complicated by the diversify of potential targets and by the need to make allocation decisions quickly. This paper describes a hybrid method applied to thermal images in order to discriminate targets with tracks from those having wheels. The hybrid method employs adaptive resonance theory neural networks and a fuzzy matching procedure to ... View full abstract»

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  • Artificial neural networks and data fusion as a biomass virtual sensor

    Publication Year: 1999, Page(s):3968 - 3973 vol.6
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB)

    The ability of artificial neural networks (ANN) to learn from experience rather than from mechanistic descriptions is making them the preferred choice to model processes with intricate variable interrelations. We apply data fusion methods (one of which is ANN) to provide estimations of biomass in a fermentation process. The readings of biomass must be periodic, of the desired frequency and reliabl... View full abstract»

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  • Neural network ensemble with temperature control

    Publication Year: 1999, Page(s):4073 - 4078 vol.6
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (324 KB)

    In this paper we propose a model of neural network ensemble composed of a number of multilayer perceptrons (MLP), each with a unique expertise. Using temperature control the most appropriate ensemble member will be automatically activated for a given environment while the irrelevant members will be inhibited. The proposed temperature control will enable the neural network ensemble to work efficien... View full abstract»

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  • Further improvement of adaptive supervised learning decision (ASLD) network in stock market

    Publication Year: 1999, Page(s):3860 - 3865 vol.6
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (404 KB)

    We apply a neural network model called adaptive supervised learning decision network (ASLD), proposed by Xu and Cheung (1997), that maximize the expected return. In generating the trading signals for training the neural network used in the ASLD system, besides maximizing the profit gain, we have also applied the portfolio technique related to Sharpe ratio (1994) which consider expected risk in add... View full abstract»

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  • A modified dynamic non-singleton fuzzy logic system for nonlinear modeling

    Publication Year: 1999, Page(s):4357 - 4361 vol.6
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (240 KB)

    This paper presents a modified version of dynamic non-singleton fuzzy logic systems (NSFLS). Mouzourise and Mendel (1997) originally introduced the NSFLS technique. This technique suffers from the lack of good convergence property. To remedy this and to improve the convergence rate of the NSFLS method, we introduce a fuzzy based algorithm. In order to show how this technique outperforms the dynami... View full abstract»

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  • Hybrid algorithms of multi-agent control of mobile robots

    Publication Year: 1999, Page(s):4115 - 4118 vol.6
    Cited by:  Papers (8)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (320 KB)

    Multi-agent robot system (MARS) represents a group of autonomous robots, which corporately solve common task in real time in dynamic environment. The paper describes methods of multi-agent control of MARS and neurocontrol of mobile robots-agents. To solve the problem of MARS control in real time, it is proposed to combine methods of artificial intelligence (AI) and neurocontrol. Multi-agent contro... View full abstract»

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  • Gas turbine vibration analysis with fuzzy ART neural network

    Publication Year: 1999, Page(s):4319 - 4323 vol.6
    Cited by:  Papers (1)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (360 KB)

    High-resolution turbine spectral data was analyzed using a fuzzy ART neural network. The network was configured as a novelty detector to automatically detect changes in the turbine operating characteristics as evidenced in the vibration spectrum. To accomplish reliable novelty detection of high-resolution spectral data, the characteristics of fuzzy ART with regards to prototype hyper-dimensions an... View full abstract»

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  • A hybrid multimodel neural network for nonlinear systems identification

    Publication Year: 1999, Page(s):4278 - 4283 vol.6
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB)

    An improved universal parallel recurrent neural network canonical architecture, named a recurrent trainable neural network (RTNN), suited for state-space systems identification, and an improved dynamic backpropagation method of its learning, are proposed. The proposed RTNN is studied with various representative examples and the results of its learning are compared with other results given in the l... View full abstract»

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  • Nonlinear cluster transformations for increasing pattern separability

    Publication Year: 1999, Page(s):4006 - 4011 vol.6
    Cited by:  Papers (1)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (428 KB)

    The objective of classification is to generate a nonlinear multidimensional decision boundary that partitions the pattern space into prescribed classes. However, these algorithms are successful only when the data is well distributed in their domain. In practice, patterns from different classes can be closely packed with significant overlap. Prior to classification, the data is generally preprocess... View full abstract»

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  • Mass spectral search method using the neural network approach

    Publication Year: 1999, Page(s):3962 - 3967 vol.6
    Cited by:  Patents (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (492 KB)

    This paper investigates the use of neural networks as a novel approach in the implementation of spectral library search for gas chromatography mass spectrometry. A total of 28 drugs currently under control in Hong Kong were chosen for the study. Real forensic data, which represents mass spectra obtained under various conditions ranging from good to poor, were used for training and testing. A total... View full abstract»

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  • Natural gas load forecasting with combination of adaptive neural networks

    Publication Year: 1999, Page(s):4069 - 4072 vol.6
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (296 KB)

    The focus of this paper is on combination of artificial neural network (ANN) forecasters with application to the prediction of daily natural gas consumption needed by gas utilities. A two-stage system is proposed with the first stage containing three ANN forecasters. The first forecaster is a multilayer feedforward network trained with backpropagation, the second one is another multilayer feedforw... View full abstract»

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  • TD methods applied to mixture of experts for learning 9×9 Go evaluation function

    Publication Year: 1999, Page(s):3734 - 3739 vol.6
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB)

    The temporal difference (TD) method is applied on a committee of neural network experts to learn the board evaluation function for the oriental board game Go. The game has simple rules but requires complex strategies to play well, and the conventional tree search algorithm for computer games makes a poor Go program. Thus, the game Go is an ideal problem domain for exploring machine learning algori... View full abstract»

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  • Supplementing neural reinforcement learning with symbolic methods: Possibilities and challenges

    Publication Year: 1999, Page(s):4145 - 4150 vol.6
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    Several different ways of using symbolic methods to improve reinforcement learning are identified and discussed in some detail. Each demonstrates to some extent the advantages of combining reinforcement learning and symbolic methods. These methods point to the potentials and the challenges of this line of research View full abstract»

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  • Application of techniques of computational intelligence for constructing reliable decision support systems

    Publication Year: 1999, Page(s):3856 - 3859 vol.6
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (196 KB)

    In recent years, techniques of computational intelligence such as neural networks, GAs, and etc. have been successfully applied for constructing various decision support systems. In this paper, we shall try to utilize neural networks to construct an intelligent decision support system (DSS) for dealing the Tokyo Stock Exchange Prices Indexes (TOPIX). We shall also briefly touch upon our recent tri... View full abstract»

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  • Image segmentation by artificial life approach using autonomous agents

    Publication Year: 1999, Page(s):4413 - 4418 vol.6
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB)

    In this paper, we propose an image segmentation by artificial life approach using autonomous agents. Each agent moves on the image according to some constant rules as follows; Each agent has features such as color. It moves onto a pixel which has the most similar features. It also puts pheromone on the pixels. The pheromone is based on the idea of chemical substances which have the property of kee... View full abstract»

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  • An application of spatial prediction using a fuzzy-neural network

    Publication Year: 1999, Page(s):4241 - 4246 vol.6
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (440 KB)

    Identifying spatial distribution of geology types beneath the earth surface is a problem of considerable importance while undertaking constructions of dams. Normally, borehole drilling and geo-tomography (tomography perfected for subsurface exploration) are used to collect data and infer geological distributions. However, the reliability of inference is generally poor and identifying geological di... View full abstract»

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  • Neural networks based chemical process models

    Publication Year: 1999, Page(s):3948 - 3951 vol.6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (316 KB)

    Efficient process design and online process control to within statistical limits play vital roles in quality improvement, and often offer a competitive edge in today's industry. We here investigate the use of artificial neural network (ANN) as a dynamic modeling tool. The ANN models are compared to traditional parametric regression models. The comparison covers various features offered by each mod... View full abstract»

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  • A fuzzy classifier based on probabilistic relaxation

    Publication Year: 1999, Page(s):4351 - 4356 vol.6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB)

    In this paper, a new fuzzy classifier is developed in which a simple relation between probabilistic vectors and fuzzy sets is derived and the probabilistic relaxation scheme is employed. The fuzzy classifier consists of two stages. Firstly, the fuzzy sets are separated into several groups in terms of the relation between probabilistic vectors and fuzzy sets. Secondly, each group of fuzzy sets is f... View full abstract»

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  • A neural network endowed with symbolic processing ability

    Publication Year: 1999, Page(s):4054 - 4058 vol.6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (408 KB)

    We propose a neural network method for the generation of symbolic expressions using reinforcement learning. According to the proposed method, a human decides on the kind and number of primitive functions which, with the appropriate composition (in the mathematical sense), can represent a mapping between two domains. The appropriate composition is achieved by an agent which tries many compositions ... View full abstract»

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  • Evaluation and identification of lightning models by artificial neural networks

    Publication Year: 1999, Page(s):3816 - 3820 vol.6
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalised from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute elec... View full abstract»

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