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Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Date 15-19 July 2001

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  • International Joint Conference on Neural Networks - Addendum [front matter]

    Publication Year: 2001
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  • Automated genotyping: combining neural networks and decision trees to perform robust allele calling

    Publication Year: 2001
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (409 KB) | HTML iconHTML

    The Human Genome Project has revolutionized the field of modem genetics by pmwiding increasingly dense high resolution genetic mops of the human genome. In order to investigate inheritance patterns of genetic disorder, selected areas of the genome are genotyped using densely distributed genetic markers. Due to the complezity associated with most of these inheritance pattems hundreds of thousands o... View full abstract»

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  • Constraining learning linear neural networks for inverting of complex matrices

    Publication Year: 2001
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (270 KB) | HTML iconHTML

    This paper proposes using linear feedfonvard neural networks trained by constrained learning algorithm (CLA) to find the inversion of nonsingular complex matrix (A) . Where the corresponding constraint relations for this problem is just AA' = I (where A' is the inverse matrix of A and I is the identity matrix). Finally, some experimental results are reported and discussed. View full abstract»

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  • Finding complex roots of polynomials by feedforward neural networks

    Publication Year: 2001
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (310 KB) | HTML iconHTML

    This paper extended the finding real roots of polynomials by feedforward neural nehvorks (FNN) with constrained learning algorithm (CLA) to finding complex roots of arbitrary polynomials (including complex ones). Likewise, for those high order complex polynomials, this network model is also extended to one which fin& recursively a small number of complex roots af a polynomial (less than the total ... View full abstract»

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  • Multi-resolution distributed ART neural networks

    Publication Year: 2001, Page(s):A19 - A24
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (468 KB) | HTML iconHTML

    This paper proposes a new neural network model, Multi-Resolution Distributed ART (MRD-ART), which employsfast stable learning and efjicient parallel matching to solve complex data classification problems. The architecture of MRD-ART network preserves the prominent characteristics of the ART networks and extendr their capability to represent input patterns in a hierarchical fashion which effectivel... View full abstract»

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  • Neural networks with problem decomposition for finding real roots of polynomials

    Publication Year: 2001
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (314 KB) | HTML iconHTML

    This paper proposes applying feedforward neural networks (FNN) with problem decomposition and constrained learning to finding the real roots of polynomials. In order to alleviate ihe load of the computational complexity for high order polynomials, this network model is extended to one which works recursively with a small number of the real roots of a polynomial (less than the total number of roots... View full abstract»

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  • Solving linear simultaneous equations by constraining learning neural networks

    Publication Year: 2001
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (284 KB) | HTML iconHTML

    This paper proposes using constrained learning algorithm (CLA) to solve linear equations, where the corresponding conrtraint relations for this problem is just the linear equations. As a result. the CLA can be effectively and appropriately applied It was found in experiments that the convergent speedfor this CLA is much faster than the recursive least square back propagation (RLS-BP) algorithm. Fi... View full abstract»

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