## Fifth International Conference on Artificial Neural Networks, 7-9 July 1997Proceedings of a July 1997 conference, in sections on algorithms, signal and image processing, Beyesian networks, self-organizing systems, system identification and control, hardware, and applications. Subjects include characterizing complexity in a radial basis function network, neural networks and seasonal time-series prediction, hierarchical models for data visualization, system identification using self-organizing feature maps, autonomous control of robots by connectionist experts, capacity of boolean associative memory, and a neural relaxation technique for chemical graph matching. No index. Annotation copyrighted by Book News, Inc., Portland, OR. |

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### Contents

a novel algorithm for topographic projections | 7 |

Mixtures of principal component analyzers | 13 |

Characterising complexity in a radial basis function network | 19 |

Copyright | |

46 other sections not shown

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algorithm application approach approximation Artiﬁcial Neural Networks back-propagation Bayesian binary classiﬁcation clusters complex components conﬁguration context convergence covariance matrix data points data set data space database deﬁned deﬁnition density distribution dynamic engine equation error bars estimate extracted FANN ﬁeld ﬁgure ﬁnal ﬁnd ﬁrst ﬁxed ﬂow frequency fuzzy Gaussian hidden layer hidden nodes hyperbox identiﬁcation IEEE IEEE Trans input space input vector iteration leaming learning linear lung boundary Lyapunov exponent match matrix measure method mixture model neural network neurons noise non-linear normalisation obtained optimisation output layer paper parameters Parzen windows pattem pattern performance pixels posterior probability prediction principal curve probabilistic problem Radial Basis Function RBF network region robot samples shear stress shown sigmoidal function signal signiﬁcant simulation speciﬁc structure technique Theorem threshold tion training data training set update values variables wavelet weights