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基于神經(jīng)網(wǎng)絡(luò)的提拉法鈦單晶生長(zhǎng)過(guò)程建模
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TP212 TH166

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Modeling of Czochralski Single Crystal Growth Process Using Neural Network
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    摘要:

    用神經(jīng)網(wǎng)絡(luò)建立提拉法鈦單晶生長(zhǎng)過(guò)程的經(jīng)驗(yàn)?zāi)Ｐ停⑼ㄟ^(guò)試驗(yàn)驗(yàn)證模型的有效性。改進(jìn)鈦單晶生長(zhǎng)試驗(yàn)設(shè)備，采集建立經(jīng)驗(yàn)?zāi)Ｐ退璧臒o(wú)噪實(shí)驗(yàn)數(shù)據(jù)。建立前饋神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)器，建模提拉法鈦晶體生長(zhǎng)過(guò)程非線性動(dòng)態(tài)特性，用自適應(yīng)BP算法訓(xùn)練神經(jīng)網(wǎng)絡(luò)，以加快網(wǎng)絡(luò)的學(xué)習(xí)和收斂。

    Abstract:

    First known attempt to empirically modeling and experimentally verifying the growth of ilmenite single crystals using Czockralski process is presented. Czochralski is an industrial crystal pulling process extensively used for silicon and germanium single crystal growth. The experimental apparatus for ilmenite growth process has been significantly improved, and applied to acquisition of noise-free experimental data for empirical modeling. A feedforward multilayer perceptron is used to develop a single-step predictor, modeling the thermal response of the Czochralski single crystal growth process of ilmenite. The training of the neural network is performed using adaptive back-propagation, an accelerated learning algorithm.

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.基于神經(jīng)網(wǎng)絡(luò)的提拉法鈦單晶生長(zhǎng)過(guò)程建模[J].鈦工業(yè)進(jìn)展,2006,23(1):21-23.

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  • 收稿日期:2005-05-20
  • 最后修改日期:2005-05-20
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