convergence of signals 中文意思是什麼
convergence of signals
解釋
信號的會聚- convergence : n. 1. 聚合，會聚，輻輳，匯合。2. 集合點；【數、物】收斂；【生物學】趨同（現象）。
- of : OF ＝Old French 古法語。
- signals : 信號
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As a result, controllers cannot output signals according to the computation result of algorithm, original iterative rules are affected, and it even affects the convergence of the algorithm. in this paper, discussion of this problem was presented
這樣控制器就無法按照迭代學習演算法的計算結果正常輸出，迭代學習控制律原有的迭代關系被破壞，並有可能破壞迭代學習控制演算法的收斂性。 -
The physics meaning of these two corollaries is explained by virtual of the generalized functions " weak - convergence theory. there exist some disadvantages such as low measuring resolution and high calculating amount for multi - scale wavelet transform when the noise in signals is higher
與一般基於多尺度分解的小波變換的檢測方法相比，這種新的函數檢測函數可以在保證檢測精度的同時，拓寬小波變換中用於信號檢測的尺度范圍。 -
By studying the discrete fourier transform properties of the band - limited digital signal, the authors introduce alternating projection neural networks into the paper, expand apnn ' s application scope from real field to complex field, and present several important conclusions on apnn. analyzing and discussing network ' s tolerance to noise, convergence rate and the spectral leakage problem of the truncated signal expected to be extrapolated by using these conclusions, the paper presents an extrapolation algorithm for band - limited signals based on alternating projection neural networks. a lot of simulation experiments show that the algorithm is effective. in addition, the algorithm is also effective to spectrum extrapolation. owing to adopting network structure, the algorithm is prone to parallel computation and vlsi design, and consequently can satisfy real time military processing needs
本文通過對頻帶受限數字信號的離散傅立葉變換特性的研究，引進了交替投影神經網路，並將其應用范圍從實數域拓廣到復數域，且給出了在復數域仍然成立的若干結論.運用這些結論，在對網路噪聲抑制、網路收斂速度及待外推信號因截斷而造成頻譜嚴重外泄問題的分析與討論的基礎上，提出了一種基於交替投影神經網路的外推演算法.模擬實驗表明該方法是行之有效的.另外，該演算法對頻譜外推同樣適用；由於它採用全互連神經網路結構，易於并行計算和vlsi實現，從而可滿足軍事上實時處理的需要 -
The filtering algorithm of neural network proposed by this paper possess higher accuracy, faster convergence, very robust to noise - contaminated teaching signals
神經網路濾波演算法具有較高的精度，收斂性好，具有抗噪聲能力，因此具有較高的魯棒性。
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