Spelling suggestions: "subject:"beural networks (computer)"" "subject:"aneural networks (computer)""
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Fabric defect detection by wavelet transform and neural networkLee, Tin-chi., 李天賜. January 2004 (has links)
published_or_final_version / abstract / toc / Electrical and Electronic Engineering / Master / Master of Philosophy
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Fast evoked potential estimation by artificial neural networks馮順明, Fung, Shun-ming. January 1999 (has links)
published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy
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273 |
Fabric surface inpection by fourier analysis and neural network陳志豪, Chan, Chi-ho. January 2001 (has links)
published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy
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A reconfigurable neural network for industrial sensory systems梁耀祥, Leung, Yiu-cheung. January 2000 (has links)
published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy
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Resource management for handoff control in wireless/mobile networks using artificial neural networksHe, Changhua, 何昌華 January 2001 (has links)
published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy
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High impedance fault detection and overvoltage protection in low voltage power systems袁綺珊, Yuen, Yee-shan, Cherry. January 1998 (has links)
published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy
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Hydrological applications of MLP neural networks with back-propagationFernando, Thudugala Mudalige K.G. January 2002 (has links)
published_or_final_version / Civil Engineering / Master / Master of Philosophy
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Nonlinear models for neural networks.Brittain, Susan. January 2000 (has links)
The most commonly used applications of hidden-layer feed forward neural networks are to fit curves to regression data or to provide a surface from which a classification rule can be found. From a statistical viewpoint, the principle underpinning these networks is that of nonparametric regression with sigmoidal curves being located and scaled so that their sum approximates the data well, and the underlying mechanism is that of nonlinear regression, with the weights of the network corresponding to parameters in the regression model, and the objective function implemented in the training of the network defining the error structure. The aim ofthe present study is to use these statistical insights to critically appraise the reliability and the precision of the predicted outputs from a trained hiddenlayer feed forward neural network. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2000.
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Neural networks constructed using families of dense subsets of L[subscript]2(R) functions and their capabilities in efficient and flexible trainingKuai, Wenming 08 1900 (has links)
No description available.
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The application of artificial neural networks for end-point trajectory control of flexible-link manipulatorsRegister, Andrew H. 08 1900 (has links)
No description available.
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