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- The influences of experiment design by steps on RBF network metamodel were also measured by numerical tests.The training sampling set of RBF network wss decided upon by uniform design. 最后采用徑向基神經(jīng)網(wǎng)絡(luò )替代模型，以均勻試驗設計為例，檢驗分步試驗優(yōu)化設計方法的有效性。
- Data from mechanical models computing and fields sampling are gathered to generate training samples with good orthogonality and wide ranges, which can improve the generality and reliability of models. 將機理模型計算數據與現場(chǎng)采集數據相結合，獲得正交性和完備性較好的訓練樣本數據，增強模型的外推能力和可信度。
- In this method, rule intensity is defined according to the number of misclassified training samples. 該算法根據誤分類(lèi)訓練樣本的數量定義規則強度。
- SVM is used to classify, which weakly depends on the quantity and quanlity of training samples. 在分類(lèi)器設計方面,選用了對樣本數量和質(zhì)量依賴(lài)性小的支持向量機。
- Besides that, we adopted a bootstrapping method during network's training, successfully solving the deficiency of non-face training samples. 而改進(jìn)后的BP網(wǎng)絡(luò )縮短了學(xué)習時(shí)間,提高了學(xué)習效率,并在一定程度上避免了學(xué)習中的局部極小問(wèn)題。
- To classify the collectivity, uaually a training sample is needed.The classification and statistical indexes of the training sample are known. 為了對總體分類(lèi)，一般應該有訓練樣本，它的分類(lèi)和統計指標都是已知的。
- To address the quality problem of training samples, this article uses sample weightiness analysis to select training samples. 為了解決訓練樣本質(zhì)量過(guò)差的問(wèn)題，本文通過(guò)重要性分析方法進(jìn)行訓練文本選擇。
- The discrimination model is established from the training samples using BP algorithm,and then the samples is distinguished from the well-trained. 利用BP算法對訓練樣本進(jìn)行學(xué)習,確定判別模型,根據已訓練好的神經(jīng)網(wǎng)絡(luò )對樣本進(jìn)行判別。
- In the training of the neural network model (NNM) of the plant and the neural network controller (NNC), training samples are got from the state function of the plant. 在訓練實(shí)現對象模型的網(wǎng)絡(luò )和實(shí)現控制器的網(wǎng)絡(luò )時(shí),由狀態(tài)方程產(chǎn)生訓練樣本。
- An automatic text categorization mechanism based on CBR was presented,the training sample library was converted to the case library and the document was classified by KNN. 文中提出了一種基于CBR的文本自動(dòng)分類(lèi)方法,先用聚類(lèi)方法把訓練樣本庫轉換為范例庫,然后用KNN思想分類(lèi)。
- The method divides theoriginal data into two parts in term of "close" degree between the original sample and forecast sample: one is initial sample, the other is training sample. 本方法對原有的樣品數據根據與待預測樣品的關(guān)系的“密切”程度分為兩個(gè)部分,一部分是初始樣品,一部分是訓練樣品。
- The denominator of generally neural network output often tends to be zero,leading to infinite loop when training sample data.The reliability of results is debased. 常規神經(jīng)網(wǎng)絡(luò )在當訓練樣本時(shí)分母項易趨于0,導致運算進(jìn)入死循環(huán),降低了結果的可信度。
- In this paper the training samples, training method of neural network and the way combined with ADRC is analyzed, and the valuable conclusion is obtained. 文中對神經(jīng)網(wǎng)絡(luò )的訓練樣本、方法及其與自抗擾控制器結合的方式進(jìn)行了分析和討論，并得出了有益的結論；
- Experiment result shows that as reserving typical samples and reducing training samples, the generalization performance and training efficient of the classifier are guaranteed. 仿真結果證實(shí)，由于保留了典型樣本，減少了訓練樣本數量，從而保證了分類(lèi)器的性能且訓練效率較高。
- To boost the recognition performance in this one training sample application scenario, we extract context information as another cue for recognizing people. 為了在這單一訓練樣本的情況下提高人臉?lè )诸?lèi)的識別性能，我們亦由輸入的照片集中，萃取出前后文訊息，來(lái)做為分類(lèi)判斷的另一種線(xiàn)索。
- Hard training will fit them to run long distances. 嚴格的訓練將使他們能跑長(cháng)距離。
- The algorithm can obtain the better classifiers by using less training samples,so it leads to more generalization and less training samples than the other learning models. 該算法能用較少的訓練樣本獲得更佳的分類(lèi)器,因此它的推廣能力較好,且對訓練要求的樣本數也大大下降。
- She earned her place in the team by training hard. 她由于刻苦訓練而在隊里取得了地位。
- In order to prevent causing network incorrectly incline with one of fault type after training, the number of training samples for every fault should be allocated averagely. 為了防止人為的造成訓練后的網(wǎng)絡(luò )過(guò)多的傾向于某一故障類(lèi)型，各故障類(lèi)型的訓練樣本數量不應相差太多。
- Its weighted training error and scaling factor cm is computed (step 3b).The weights are increased for training samples, which have been misclassified (step 3c). 計算錯誤率和換算系數cm(step 3b).;被錯分的樣本的權重會(huì )增加。
