連合神經元 的英文怎麼說

中文拼音 [liángěshénjīngyuán]
連合神經元 英文
commissural neuron
  • 連 : Ⅰ動詞1 （連接） link; join; connect 2 （連累） involve （in trouble）; implicate 3 [方言] （縫） ...
  • 合 : 合量詞（容量單位） ge, a unit of dry measure for grain （=1 decilitre）
  • 神 : Ⅰ名詞1 （神靈） god; deity; divinity 2 （精神; 精力） spirit; mind 3 （神氣; 神情） expression; l...
  • 經 : 經動詞[紡織] （把紡好的紗或線梳整成經紗或經線） warp
  • 連合 : coalesce; inosculation連合處 commissure; 連合分佈 joint distribution; 連合活字 logotype; 連合線 linea commissural
  • 神經 : nerve; nervus
  1. To solve these problems respectively, the color space transformation and bp neural network are firstly used to realize the classification and threshold processing of images. then the images processing including thinning, interval linking, code word chaining, seed filling, boundary fitting is performed well by some methods in mathematical morphology and computer graphics and interpolation in numerical value analysis

    為了逐一解決這些困難，運用了色彩空間變換以及bp神經元網路的方法對圖像進行分類和閾值處理，利用數學形態學和計算機圖形學以及數值分析中的插值等方法對圖像進行了細化、間隙連接、鏈碼、種子填充、邊界擬合等處理。
  2. In order to find out a novel fuzzy inference algorithm being superior to cr1 algorithm and a novel fuzzy neural network, the author deduces nine kind of triple i algorithms by nine fuzzy operators. further more, the author compares these algorithms with the ordinary intuitional rules respectively and gives a conclusion that rl - type triple i algorithm is rather more closely to the intuitional rules. because fuzzy neural networks require the conditions of continuous and differential on membership functions of fuzzy sets and fuzzy operators of fuzzy inference, by means of comparison study, the author obtains a conclusion that the fuzzy neural networks with production operator and cr1 inference algorithm are very fit to study the problem of control

    為了找出優于cri演算法的模糊推理方法和新型模糊神經網路，本文首先對常用九種模糊運算元給出了具體三i演算法，並從模糊推理的另一個方面，考察了各種演算法對日常直觀模糊推理規則的貼近程度，得出「 r _ l型三i演算法貼近日常直觀推理規則」這一結論；同時，考慮到模糊神經網路要求「網路輸出保持對模糊運算元和隸屬函數連續性、可微性」這一特性，通過比較研究，得出利用乘積運算元和cri推理的模糊神經網路最適合控制問題。
  3. Firstly, the paper, combining the characteristic of synchronous pulse bursts and inhibition with the modified pcnn model, presents a way of finding the foveation points in the images adaptively and effectively, and simulates the human vision system. secondly, pcnn is extended to pcnns, based on the properties of information couple and transmission, an algorithm that is used to fuse images of the same target got by several sensors to an image is presented to simulate the human vision system. thirdly, combining the properties of synchronous pulse bursts, capture, and transmission and competition of waves, the paper presents two ways of classification, one is an algorithm based on the properties of neuron to capture and inhibit to classify the data taking on any complex unlinear distribution robustly, the other is based on the restricted distance and modified of the former to remove the influence of inferior samples in classification ; fin ally, based on the accumulative difference pictures, and the forming and transmission of pcnn wave, selecting and controlling the direction of autowave by connecting the neighbouring neurons selectively, the paper presents a way to simulate the tracks of moving object and detect the moving direction

    首先結合pcnn的同步脈沖發放和側抑制特性，提出了基於改進型pcnn的圖像凹點檢測演算法，該演算法是一種自適應而有效的圖像凹點檢測方法，並且較好地模擬了人類視覺系統；然後，結合信息傳遞和信息耦合特性，將pcnn擴展成pcnns （ pcnn網路群） ，提出了一種基於pcnns的圖像融合演算法，能夠將多個傳感器獲取的同一目標的圖像信息融合到一幅圖像中，有效模擬了人類視覺系統；另外，結合pcnn的同步脈沖發放特性、捕獲特性和波的傳播競爭特性，開拓地將pcnn用於模式分類中，提出了基於耦合神經元點火捕獲抑制特性的分類方法和改進的約束距離下的pcnn分類方法，前者可實現對樣本空間中任意復雜分佈訓練樣本的穩健非線性分類，而後者能夠消除訓練樣本中刺點對分類的影響；最後，結合累積差分圖像思想、 pcnn波的形成與傳播特性，通過各神經元之間連接取向來選擇與控制自動波的流向，將pcnn用於運動視覺分析中的運動軌跡模擬及運動方向檢測。
  4. Signal layer represent links between the nerve cells, knowledge layer represent rules and reasoning of expert, conception layer is the medium layer and connect signal layer and knowledge layer, therefore integrated symbol computing ability of es with link features of ann into one body to make brain model more intelligent

    刺激層體現神經元的連接，知識層體現專家的規則、推理，從而將專家系統的符號計算和神經網路的連接主義兩者有機的結合，並在其基礎上構建了形體的表徵模型、推理方法，實現了形態構成知識的產生，為人腦模型的「智能」能力的實現和應用奠定了基礎。
  5. Combining, switching adaptive control and neural network, a new adaptive control algorithm which can adjust continuously is proposed. the tracking and convergence properties of the closed loop are proved

    摘要將開關自適應控制與神經元網路相結合，提出了一種新的能連續調整的自適應魯棒控制演算法，給出了其閉環系統的跟蹤收斂性證明。
  6. A neuron is like a microprocessor chip in that it receives thousands of signals through its dendrites and constantly integrates all the input it receives from these connections

    從樹突接收到的訊號可是成千上萬，神經元像個微處理器晶片，隨時都在整合來自這些連線的所有輸入訊號。
  7. Based on analyzing the relationship between linear separability and a connected set in boolean space, the particular effect of a restraining neuron in extraction of rules from a bnn is discussed, and that effect is explained through a example called a mis problem in boolean space. in this paper, a pattern match learning algorithm of bnns is proposed. when a bnn has been trained by the algorithm, all the binary neurons of hidden layer belong to one or more ls series, if the logical meanings of those ls series are clear, the knowledge in the bnn can be dug out

    另一個研究成果是在分析線性可分和樣本連通性關系的基礎上，以mis問題為例，討論了抑制神經元在二進神經網路規則提取中的獨特作用，提出了二進神經網路的模式匹配學習演算法，採用這種演算法對布爾空間的樣本集合進行學習，得到的二進神經網路隱層神經元都歸屬於一類或幾類線性可分結構系，只要這幾類線性可分結構系的邏輯意義是清晰的，就可以分析整個學習結果的知識內涵。
  8. In terms of our results, it is hypothesized that in the central auditory system when the sound information is conducting through continuous synaptic clefts, there are interactions and integrations occurring between the ascending and corticofugal descending pathways with neural inhibition or facilitation so as to realize the neural integration that diverging or converging sound information with new forms, which ensures the neurons tune the sound information with biological significance

    據此推測，當神經元的信號在不同聽中樞結構中通過連續的突觸連接時，上行性或離皮層下行性神經抑制或神經易化通路之間在不斷地發生相互作用與整合，從而使得聲信息以新的方式分散或聚合，實現新的神經整合過程，以保證神經元調諧有生物學意義的聲信號。
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