equalization network 中文意思是什麼

equalization network 解釋
均衡網路
  • equalization : n. 相等；均等；平均。 equalization of landownership 平均地權。
  • network : n. 1. 網眼織物。2. （鐵路、河道等的）網狀系統，網狀組織，廣播網，電視網，廣播[電視]聯播公司。3. 【無線電】網路，電路。4. 【計算機】電腦網路，網。
  1. This paper formulize the foundational principle and development of blind equalization technique based on neural network. the traditional neural network blind equalization algorithms have many advantages and disadvantages. through analyse these disadvantage, two kinds of new blind equalization algorithm based on neural network is proposed

    本文首先對基於神經網路的盲均衡技術原理及發展進行了闡述，分析了原有神經網路盲均衡演算法的優缺點，然後對原有演算法的缺陷進行改進，提出了兩類新的神經網路盲均衡演算法。
  2. The paper particularly analyzed the satellite resource management structure, satellite connect admission control and mac ( media access control ) protocol. then the proposed delay equalization algorithm was applied in this satellite network

    論文通過對衛星網路資源調度管理結構、衛星接入以及mac （ mediaaccesscontrol ）協議的研究，將提出的基於時延均衡的調度演算法在衛星網路中實現。
  3. Therefore this paper proposes the delay equalization algorithm which can both guarantee the qos of high priority traffic and effectively reduce the delay of ubr traffic in broadband satellite network. it is based on the prediction and equalization the delay of vbr or abr traffic so that the saved slots can be allocated to ubr traffic. the approach of dynamic alternation slots between vbr and ubr traffic can improve the mean tdma frame utilization while reducing the delay of ubr traffic

    因此本文在基於流量估計的資源調度演算法基礎上，提出了一種在寬帶衛星網路下能夠保證高級別類業務qos ，同時又能夠有效地降低ubr業務時延的時延均衡（ delayequalization ）調度演算法，它是基於對vbr或abr業務時延的預測，均衡時延的方法，保證vbr或者abr業務qos的同時，將節省的時隙分配給ubr業務。
  4. The second is blind equalization algorithm based on bili near recurrent neural network. this algorithm adopt the bilinear recurrent neural network and design a new transmission function and cost function. through computer simulation, all proposed algorithm have better convergence performance

    第二類是基於反饋神經網路（ rnn ）的盲均衡演算法，此類演算法使用了一種新型的雙線性反饋神經網路（ blrnn ） ，並將這種網路結構擴展到了復數域內，然後依據盲均衡的特點為網路設計了傳輸函數和代價函數。
  5. In chapter 3 a complex - valued neural network method for adaptive complex communication channel equalization is applied. after comparing the equalization result of gmsk and 4 - qam signal, it shows that the method is effective when gmsk signals pass the complex communication channel and such structure can save the hardware source

    第三章進一步引申第二章的內容，採用了一種復神經網路的自適應均衡的方法，通過比較gmsk和4 - qam信號的均衡結果，表明這種方法對gmsk信號通過復通道均衡的有效性，並且所採用的結構可以節省硬體資源。
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