階乘級數 的英文怎麼說
中文拼音 [jiēchéngjíshǔ]
階乘級數
英文
factorial series- 階 : 名詞1. （臺階） steps; stairs 2. （等級） rank 3. [醫學] （耳蝸的三個螺旋管的任一個） scala 4. [數學] order 5. [地質學] stage
- 級 : Ⅰ名詞1 （等級） level; rank; grade 2 （年級） any of the yearly divisions of a school course; gra...
- 數 : 數副詞（屢次） frequently; repeatedly
- 級數 : [數學] progression; series; number of stages; number of steps; stage number級數變換 transformatio...
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One is to use fourier transformation to convert the source signal from time domain to frequency domain and to discard high frequency harmonious components upwards of 19 ( gb / t14953 - 93 d5. 3 demanding ), then to have static huffman coding to the quantized char array which is composed of reserved direct current component and basic wave and each high frequency " s amplitudes and angles. the other is to use discrete wavelet transformation to convert the source signal from time domain to frequency domain and to set the high frequency coefficients that its absolute value is smaller than the given threshold to zero, then to have dynamic huffman coding to the quantized char array which is composed of multiple, wavelet ' s level, datum length, low frequency coefficients and reserved high frequency coefficients. mass simulinks and analyses under the two circumstances have done to show that data compression ratio is small and the relative error is also small and within the permission of engineering and the compression problem can be solved in theory of measured datum of power system
第一種情況的壓縮方法為：採用傳統的傅立葉變換把原始信號從時間域變換到頻率域，舍棄20次及其以上的高次諧波成分（保證了gb / t14953 ? 93d5 . 3要求） ，然後對保留的直流分量、基波和各次諧波的幅值和相角數據量化后和量化時分別乘以的倍數系數構成一個數組，以字元形式保存，採用靜態huffman編碼對變換數據進行壓縮；採用離散小波變換把原始信號從時間域變換到頻率域，然後對分解得到的高頻系數進行閾值量化處理，對乘以的倍數系數、小波變換的階數、小波變換后的低頻、各級高頻以及原始數據長度、量化后的低頻系數以及保留的高頻系數大小、位置構成一個數組，以字元形式保存，採用動態huffman編碼對這個文件進行壓縮。So we adopt an radial basic function neural network and a three - order volterra series filter to construct two prediction model which are used to predict the eeg signal. in this paper, we improve the radial basic function neural network and breach the two - order limited by using product - coupling to realize the three - order volterra series filter. wavelet transform is an analytical method that unites the time and frequency domain
因此，本文採用徑向基函數神經網路和三階volterra級數濾波器分別構造腦電信號的兩個預測模型；對通常的徑向基函數預測網路作了改進，並且採用乘積耦合近似實現三階volterra級數濾波器，突破了常規上只能用二階volterra級數濾波器的限制。
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