×

# minimum problem中文

• 極小問題
• 極小值問題

### 例句與用法

1. The statistical learning theory and support vector machine have been introduced . the model selection , over - learning , nonlinear , dimensions curse and local minimum problems have been researched
介紹了統計學習理論和支持向量機方法，研究分析了機器學習方法中存在的模型選擇、過學習、非線性、維數災難和局部極小點等問題。
2. A new genetic neural network is to conduc the non - rigid modeling , which is first put forward , in accordance with some drawbacks of the current software compensation , which include low accuracy and trouble some expressions . and the current difficult questions , which include local minimum problem and the selection of the number of neural network hidden units , are done
針對現有誤差補償過程中精度較低等問題，提出了一種改進的遺傳神經網絡算法建立三坐標測量機非剛性誤差模型，這種算法克服了現有bp算法神經網絡建模存在的局部極小點、網絡隱單元數選擇等問題。
3. We proposed an improved simulated annealing algorithm with neighbor function based on self - optimization of scale parameter . furthermore incorporating disaster - modification and the improved annealing into genetic algorithm , an improved genetic - annealing algorithm is proposed . in order to solve the deceptive minimum problem , an improved evolutionary strategy combined with similarity detection and improved mutation operator
提出了鄰域尺度函數自尋優的模擬褪火算法，結合遺傳算法，引入災變算子，提出了改進遺傳模擬褪火算法；為了解決尋優過程中的最小欺騙問題，我們提出了相似性檢測，結合改進的適應值無關變異算子，提出了基于相似性檢測和適應值無關變異算子的進化策略算法。
4. 2 . on the base of detailedly analysing the fourier neural networks , we find this neural networks have the characteristic which can transform the nonlinear mapping into linear mapping . so , we improve the original learning algorithm based on nonlinear optimization and propose a novel learning algorithm based on linear optimization ( this dissertation adopts the least squares method ) . the novel learning algorithm highly improve convergence speed and avoid local minima problem . because of adopting the least squares method , when the training output samples were affected by white noise , this algorithm have good denoising function
在詳細分析已有的傅立葉神經網絡的基礎上，發現傅立葉神經網絡具有將非線性映射轉化成線性映射的特點，基于這個特點，對該神經網絡原有的基于非線性優化的學習算法進行了改進，提出了基于線性優化方法(本文采用最小二乘法)的學習算法，大大提高了神經網絡的收斂速度并避免了局部極小問題；由于采用了最小二乘方法，當用來訓練傅立葉神經網絡的訓練輸出樣本受白噪聲影響時，本學習算法具有良好的降低噪聲影響的功能。