gaussian network 中文意思是什麼
gaussian network
解釋
高斯網路-
The other is an improved fuzzy neural network that is joined a compensative operation layer and adopts the improved center - or - gravity defuzzication in feedforward fuzzy neural network based on gaussian fuzzy logical system
其二是改進的補償模糊神經網路。該網路在基於高斯模糊邏輯系統的前饋模糊神經網路中加入了補償運算層和採用了改進型中心反模糊化器。 -
An indirect self - adaptive fuzzy - neural network controller ( fnnc ) has been proposed with its parameters and the structure tuned simultaneously by ga in virtue of the powerful optimization property of ga. the structure of the controller is based on the radical basis function ( rbf ) neural network with gaussian membership functions. the performance of the proposed fnnc is compared with a conventional fuzzy - pid controller and the simulation results show that the fnnc presents encouraging advantages
針對神經網路採用一維反向傳播訓練演算法速度較慢且易於陷入局部極小點的不足，設計了一種間接自校正模糊神經網路控制系統，利用遺傳演算法（ ca ）對隸屬度函數的結構和參數進行優化，模擬比較表明該控制比模糊pid控制具有更優的性能。 -
Their learning and training rules have been analyzed profoundly and their abilities to approximate arbitrary nonlinear function have been testified and compared by the simulation. a new rbf neural network has been presented which uses a raised - cosine function as activation transfer function. it provides a wider generalization in comparison with gaussian rbf neural networks by simulation as well as strong approximation ability, fast convergence, a rule to select the parameters of the networks
本文詳細研究了兩種典型的前向神經網路（ bp網路和rbf網路）的學習和訓練演算法，提出了一種新穎的基於緊支集餘弦函數的徑向基神經網路，其克服了常用的高斯型rbf神經網路雖具有緊支集但各基函數非正交的不足，其收斂速度快、網路參數選取有理論依據且相比于高斯型rbf神經網路具有更強的泛化能力，模擬驗證了其有效性。 -
Taking 1951 ~ 2000 monthly rainfall data in the zhanjiang area as the time series and using the gaussian radial base function and a delayed input window chosen at 6, a new intelligent forecast system is developed based on radio basic function neural network ( rbfnn ) to predict monthly rainfall from 1991 to 2003
摘要以湛江地區50年來的月降水量為時間序列，利用高斯徑向基函數，選擇輸入窗口（時滯）大小為6 ，建立了一種智能型的徑向基函數神經網路預測系統，並分別對1991 ~ 2000年和2001 ~ 2003年的月降水量進行了測試預報和獨立樣本預測。 -
By adopting the real - time rescaled range ( rrs ) algorithm developed from the rs method, we do the simulation work using fractional gaussian noise ( fgn ) and real network traffic data collected from lan and wan. it shows the method we bring up can differentiate normal network traffic and ddos attack traffic effectively and precisely in most situation, and has provided a new way to detect and prevent ddos attack duly and precisely
通過基於rs演算法改進的實時hurst系數估計演算法rrs ，採用分形高斯噪聲和局域網、廣域網真實業務數據進行了模擬實驗，結果表明本文所提出的方法可以在絕大多數情況下準確高效地區分正常網路業務和包含了ddos攻擊的數據業務，從而為及時、準確地判斷和制止大規模ddos攻擊的發生提供了新的手段。
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