例句與造句

1. 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(shù)自尋優(yōu)的模擬褪火算法，結(jié)合遺傳算法，引入災(zāi)變算子，提出了改進(jìn)遺傳模擬褪火算法；為了解決尋優(yōu)過程中的最小欺騙問題，我們提出了相似性檢測，結(jié)合改進(jìn)的適應(yīng)值無關(guān)變異算子，提出了基于相似性檢測和適應(yīng)值無關(guān)變異算子的進(jìn)化策略算法。
2. 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(xì)分析已有的傅立葉神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)上，發(fā)現(xiàn)傅立葉神經(jīng)網(wǎng)絡(luò)具有將非線性映射轉(zhuǎn)化成線性映射的特點，基于這個特點，對該神經(jīng)網(wǎng)絡(luò)原有的基于非線性優(yōu)化的學(xué)習(xí)算法進(jìn)行了改進(jìn)，提出了基于線性優(yōu)化方法(本文采用最小二乘法)的學(xué)習(xí)算法，大大提高了神經(jīng)網(wǎng)絡(luò)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(dāng)用來訓(xùn)練傅立葉神經(jīng)網(wǎng)絡(luò)的訓(xùn)練輸出樣本受白噪聲影響時，本學(xué)習(xí)算法具有良好的降低噪聲影響的功能。
3. It's difficult to find minimum problem in a sentence. 用minimum problem造句挺難的