Function approximation study of general fuzzy system 模糊系統(tǒng)的函數(shù)逼近特性研究
Research and simulation of bus protection with function approximation ability 基于函數(shù)逼近能力的母線保護(hù)的研究及仿真
So the network has the better capability of function approximation and pattern recognition 因此改進(jìn)的網(wǎng)絡(luò)具有較好的函數(shù)逼近能力和模式識(shí)別能力。
After trained , the fuzzy neural networks have good function approximation ability and generalization ability 訓(xùn)練后的模糊神經(jīng)系統(tǒng)具有良好的函數(shù)逼近能力和泛化能力。
Secondly , a solution based on the theory of function approximation is proposed to select the key parameter of neural network 其次,在神經(jīng)網(wǎng)絡(luò)關(guān)鍵參數(shù)選擇問題上,提出了一種基于函數(shù)逼近思想選擇網(wǎng)絡(luò)參數(shù)的方法。
, 2002 ) . the varying coefficients model , which is the function approximation method for high dimension , is discu ssed in this paper 本論文主要討論的是變系數(shù)模型( thevaryingcoefficientmodel ) ,屬于函數(shù)近似這一類。
In addition , we formulated measures of ga - hardness for gas with real - valued encoding , and advanced the method of the first order function approximation 對實(shí)數(shù)編碼遺傳算法困難度的測試方法進(jìn)行了分析,提出了一階函數(shù)逼近測試法。
So , it could be seen that the structures research , function approximation properties and learning algorithms of procedure neural network models is quite significant 研究過程神經(jīng)元網(wǎng)絡(luò)模型的拓?fù)浣Y(jié)構(gòu),函數(shù)逼近性質(zhì),學(xué)習(xí)算法等具有十分普遍的意義。
Kosko applied it to several engineering applications and summarized that the key of fuzzy engineering was function approximation in the book of fuzzy engineering 在模糊工程一書中, kosko把這個(gè)模型應(yīng)用到許多工程領(lǐng)域并概括出模糊工程的核心問題是函數(shù)逼近問題。
While using support vector regression to do function approximation , we can control the number of support vectors and the approximative performance all by two parameters 在用支持向量回歸進(jìn)行函數(shù)逼近時(shí),我們完全可以用兩個(gè)參數(shù)來控制支持向量的個(gè)數(shù)和逼近的效果。
The need for function approximations arises in many branches of applied mathematics, and computer science in particular. In general, a function approximation problem asks us to select a function among a well-defined class that closely matches ("approximates") a target function in a task-specific way.