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TranslationMobile

參數(shù)學習

"parameter" in Chinese : n. 1.【數(shù)學】參數(shù)，變數(shù)；參詞；參項。 2.【物理學 ...
"learning" in Chinese : n. 學，學習；學問，學識；專門知識。 good at ...
"learning" in Chinese : n. 學，學習；學問，學識；專門知識。 good at learning 善 ...
"learning to be" in Chinese : 做人; 學會生存
"learning with it" in Chinese : 學習有關(guān)問題
"a-parameter" in Chinese : 孔性壓力參數(shù)
"in parameter" in Chinese : 寫入?yún)?shù)
"in-parameter" in Chinese : 輸入?yún)?shù)
"no such parameter" in Chinese : 參數(shù)不存在
"parameter" in Chinese : n. 1.【數(shù)學】參數(shù)，變數(shù)；參詞；參項。 2.【物理學】參量；(結(jié)晶體的) ...
"accretion learning concomitant learning" in Chinese : 附學習
"curve of learning; learning curve" in Chinese : 學習曲線
"learning is e-learning" in Chinese : 網(wǎng)絡(luò)學習
"a learning experience" in Chinese : 長學問
"a little learning" in Chinese : 一點學問短篇小說集

Examples

A parameter learning method for stochastic fuzzy
隨機模糊神經(jīng)網(wǎng)絡(luò)的參數(shù)學習算法
In the research on fuzzy neural networks , the problem of parameters learning is very important
在模糊神經(jīng)網(wǎng)絡(luò)的研究中，參數(shù)學習問題具有很大的重要性。
A new learning algorithm of the continue wavelet networks parameters is proposed , that is , hybrid parameters learning algorithm . the parameters of wavelet networks are divided into two parts , and different methods are used to training them . compare to the traditional parameters learning method , the new method proposed in this paper has the advantages of rapid convergence speed and high approximation capability
提出了連續(xù)小波網(wǎng)絡(luò)的混和參數(shù)訓(xùn)練方法，將小波網(wǎng)絡(luò)參數(shù)分為小波系數(shù)和網(wǎng)絡(luò)權(quán)值兩部分，對這兩部分參數(shù)分別采用不同的方法進行訓(xùn)練，這一新的訓(xùn)練方法相對于傳統(tǒng)的小波網(wǎng)絡(luò)參數(shù)訓(xùn)練方法來說具有收斂速度快，逼近精度高等優(yōu)點。
In designing a multi - structuring elements filter , combination rules and structuring elements of the morphological transform are determined automatically , and one kind of neural networks is taken for the filter , in optimzing structural parameters of the filter , three computation methods are designed respectively , by adopting some priori information in application fields to guide optimal structural parameter learning procedure , which are the bp adaptive learning algorithm , the heuristic genetic learning algorithm and the inductive simulated annealing learning algorithm
在多結(jié)構(gòu)基元濾波器設(shè)計中，通過學習人-機交互選定的目標樣本，自動確定形態(tài)變換的組合規(guī)則及其結(jié)構(gòu)元素，最終以神經(jīng)網(wǎng)絡(luò)形式構(gòu)成濾波器。在結(jié)構(gòu)參數(shù)的優(yōu)化學習中，利用應(yīng)用領(lǐng)域的先驗知識，分別設(shè)計了自適應(yīng)bp學習、啟發(fā)式遺傳學習和引導(dǎo)式模擬退火學習等三種最優(yōu)化計算方法。