共軛並向量 的英文怎麼說
中文拼音 [gòngèbàngxiàngliáng]
共軛並向量
英文
conjugate dyad-
Procreant knowledge expression and forward inference engine are adopted in the method of fault diagnosis based on expert system theory. in the fault diagnosis applying neural network theory, six kinds of improved arithmetic of back - propagation arithmetic, including gradient descent with momentum, variable learning rate back - propagation, resilient back - propagation, quasi - newton, levenberg - marquardt and conjugate gradient, are applied to diagnose the faults of electric load manage center and solid state power controller. different diagnostic results gotten by simulation are compared at last
在基於專家系統的故障診斷方法中，採用了產生式知識表達和正向推理機制；在基於神經網路的故障診斷方法中，則分別採用了bp神經網路的附加動量法、自適應學習速率、彈性bp演算法、擬牛頓法、共軛梯度法和levenberg - marquardt法對電氣負載管理中心和固態功率控制器的故障進行診斷，並對由模擬得到的不同診斷結果進行比較。( 1 ) the posterior distribution of the coefficient matrix, the precision matrix and covariance matrix, and their bayesian estimation under the matrix normal - wishart conjugate prior distribution. ( 2 ) the deduction of the predictive distribution, proved to be matrix t distribution. ( 3 ) the designs of bayesian multivariate mean value control charts in terms of the relationship between the multivariate wishart distribution and x2 distribution, the bayesian process capability index and its confidence lower limi
通過多方程模型系統的統計結構，證明了矩陣正態? wishart先驗分佈是模型參數（ ， ）的共軛先驗分佈，研究了該先驗分佈下模型系數矩陣、精度陣和協方差陣的后驗分佈及其貝葉斯估計，對模型預報密度函數進行了嚴格的數學推導，並將其應用於多元質量控制領域，構造了貝葉斯均值向量聯合控制圖；結合wishart分佈與x ~ 2分佈之間的關系，設計與推斷了貝葉斯多指標過程能力指數及其貝葉斯置信下限。The method has special obvious advantages to diagnosis the faults when several faults exist simultaneously. the paper constructs a three - layer forward neural network to diagnosis the fault and trains the network with characteristic eigenvectors extracted through the wavelet packet analysis, when training the net using the method of adding inertia item and the bp algorithm of conjugate gradient method, at the same time adapt the initial data of the network through the test
同時，文中還構造了三層的前向神經網路，以小波包分析提取的故障特徵向量作為網路的訓練樣本數據，採用加慣性因子、共軛梯度法和迭代過程中改變學習率的反向傳播演算法來對神經網路進行訓練，並採用試驗的方法調整神經網路的初始值。
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