Solution will convergent to optimal point of feasibility through penalty of solution outside feasible region again and again.

通過對可行域外的解進行懲罰亚洲黄情，經(jīng)過不斷迭代，使解群逐漸收斂于可行的最優(yōu)點大屁股网站。

It provides a theoretical basis for reliable and effective operation of the network,which is shown to be globally convergent with respect to its feasible region called quasi-convergence and to be primal in the sense that its trajectories can never escape from the feasible region.

在很一般的條件下證明了網(wǎng)絡(luò)的總體穩(wěn)定性問題，這些對于網(wǎng)絡(luò)的可靠運行與有效運行提供了理論基礎(chǔ)亚洲电影色色黄片。

The pair of finger-shaped grass-poking wheels forming splay with each other is mounted on installation rack for convergent arms, wherein the centers of the pair of finger-shaped grass-poking wheels of the front ends converge at a point and are not interfered with each other.

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OBJECTIVE: To explore the effect of different convergent conditions on accuracy of simulation results from a three dimensional finite element model of the pelvic ring.

摘要目的：探討不同收斂條件下骨盆三維有限元計算結(jié)果的準確性求a片网址。

Under fitful conditions, it was proven that this algorithm is globally convergent.

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Using a new measure function to express the sparse prior information of SAR image, a regularized model is established. The model is solved by the fixed point iteration method and proved to be convergent.

本文采用一種新的度量函數(shù)表示SAR圖像的稀疏先驗色呦呦网站直接看的，建立正則化模型日本三级片在线免费播放，證明了不動點迭代求解的收斂性。

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By using the Lyapunov functional method and matrix theory, the critical dynamics behavior of recurrent neural networks with nearest point projection has been investigated. It is shown that such networks are globally convergent and asymptotically stable when the networks are nonlinear non-dilatation.

基于Lyapunov泛函方法和矩陣理論，本文研究一類被廣泛應(yīng)用的反饋神經(jīng)網(wǎng)絡(luò)―最近點投影型反饋神經(jīng)網(wǎng)絡(luò)的臨界全局動力學(xué)性態(tài)日韩免费午夜毛片视频，當網(wǎng)絡(luò)具有非線性不擴張性質(zhì)時一级无码奶水，在臨界條件下得到該類網(wǎng)絡(luò)全局收斂性和漸近穩(wěn)定性。

In the study of the asymptotic properties, the testing statistics converges to a zero mean Gaussian process and is very sensitive to the to the local alternatives converging to the null one at a parameter convergent rate.

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In SLDMINV the invariance of the Krylov subspace is invoked, making the main computation reduced into almost real arithmetic, and thus lowering the computation amount markedly. In the meantime, the iteration is performed in the Krylov subspace generated from the inverse of the coefficient matrix and so the convergent speed is enhanced greatly, leading to two orders of the magnitude improvement over standard SLDM.

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