probabilistic neural network造句
例句與造句
- structural damage localization using probabilistic neural network
用概率神經(jīng)網(wǎng)絡(luò)進(jìn)行結(jié)構(gòu)損傷位置識(shí)別 - structural damage detection based on adaptive probabilistic neural network
自適應(yīng)概率神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)損傷檢測(cè) - fuzzy probabilistic neural network water quality evaluation model and its application
模糊概率神經(jīng)網(wǎng)絡(luò)水質(zhì)評(píng)價(jià)模型及其應(yīng)用 - application of probabilistic neural network in text-independent speaker identification
概率神經(jīng)網(wǎng)絡(luò)在文本無關(guān)說話人識(shí)別中的應(yīng)用 - radial basis function based probabilistic neural network in arrhythmia classification
基于徑向基函數(shù)概率神經(jīng)網(wǎng)絡(luò)的心律失常自動(dòng)識(shí)別 - It's difficult to find probabilistic neural network in a sentence. 用probabilistic neural network造句挺難的
- improving the recognition rate of eeg in bci based on genetic algorithm and probabilistic neural network
基于遺傳算法和概率神經(jīng)網(wǎng)絡(luò)提高腦機(jī)接口中腦電信號(hào)識(shí)別率 - delineation of coastal region of south china-with matlab to create radial basis probabilistic neural network
華南沿海潛在震源區(qū)劃分運(yùn)用matlab徑向基概率神經(jīng)網(wǎng)絡(luò)工具箱求解 - probabilistic neural network model and its application in evaluation of the water quality near the dam area of three gorges reservoir
概率神經(jīng)網(wǎng)絡(luò)水質(zhì)評(píng)價(jià)模型及其對(duì)三峽近壩水域的水質(zhì)評(píng)價(jià)分析 - finally, probabilistic neural network was employed to identify the vinegars, and the accuracy of pnn in term of predicting the vinegars was very high
利用概率神經(jīng)網(wǎng)絡(luò)對(duì)所測(cè)試的食醋進(jìn)行了識(shí)別，有較高的識(shí)別率。 - principal component analysis ( pca ), cluster analysis ( ca ) and probabilistic neural network ( pnn ) were used in the data analysis and pattern recognition
并通過主元分析、聚類分析和概率神經(jīng)網(wǎng)絡(luò)對(duì)數(shù)據(jù)進(jìn)行了分析和識(shí)別。 - probabilistic neural network ( pnn ) is a classification network, which is based on bayesian decision theory and probability function estimation theory
d.f.specht提出的概率神經(jīng)網(wǎng)絡(luò)(probabilisticneuralnetwork,pnn)是基于密度函數(shù)估計(jì)和貝葉斯決策理論而建立的一種分類網(wǎng)絡(luò) - two spatially registered images with different focuses are decomposed into several blocks . then, three features reflecting the clear level of every block, i . e ., spatial frequency, visibility, and edge, are calculated . finally, artificial neural networks, i . e ., multilayer-perceptron, radial-basis function, probabilistic neural network, are used to recognize the clear level of the corresponding blocks to decide which blocks should be used to construct the fusion result
具體實(shí)現(xiàn)過程概述如下：首先將兩幅（或多幅）配準(zhǔn)圖象進(jìn)行分塊處理，提取兩幅圖象中對(duì)應(yīng)塊的能反映圖象清晰度的三種特征，即空間頻率、可見度和邊緣，將特征歸一化后送入訓(xùn)練好的神經(jīng)網(wǎng)絡(luò)進(jìn)行識(shí)別，根據(jù)得到的結(jié)果依據(jù)“誰清晰誰保留”的原則構(gòu)成融合的圖象。 - secondly, the paper proposes probabilistic neural networks ( pnn ) methods of speaker identification, and thoroughly researches the models, training algorithms, real-time fabric, noise robustness and network structure of pnn . ( 1 ) the heteroscedastic pnn model with training algorithms that is a mixture of gaussian basis functions having different variances is considered
其次，在對(duì)說話人辨認(rèn)分類器的特性進(jìn)行深入分析的基礎(chǔ)上，本文提出了概率神經(jīng)網(wǎng)絡(luò)說話人辨認(rèn)方法，并就概率神經(jīng)網(wǎng)絡(luò)的模型、訓(xùn)練算法、實(shí)時(shí)性、噪聲魯棒性、網(wǎng)絡(luò)結(jié)構(gòu)改進(jìn)等方面進(jìn)行了深入研究。 - the main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed; the xor problem is implemented by using pnn . a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons . simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn
本文主要分析了pnn隱層神經(jīng)元個(gè)數(shù),隱中心矢量,平滑參數(shù)等要素對(duì)網(wǎng)絡(luò)分類效果的影響,并用pnn實(shí)現(xiàn)了異或邏輯問題;提出了一種新的pnn有監(jiān)督學(xué)習(xí)算法:用學(xué)習(xí)矢量量化對(duì)各類訓(xùn)練樣本進(jìn)行聚類,對(duì)平滑參數(shù)和距離各類模式中心最近的聚類點(diǎn)構(gòu)造區(qū)域,并采用遺傳算法在構(gòu)造的區(qū)域內(nèi)訓(xùn)練網(wǎng)絡(luò),實(shí)驗(yàn)表明:該算法在分類效果上優(yōu)于其它pnn學(xué)習(xí)算法 - these coherent processors can fulfill arbitrary complex parameters . chapter 4 puts forward two deinterleaving methods based on probabilistic neural network ( pnn ) . the self-organization pnn deinterleaver is suitable for unknown emitters, while the rbpnn deinterleaver is suitable for known emitters
神經(jīng)網(wǎng)絡(luò)用于脈沖列去交錯(cuò)是國內(nèi)外一直關(guān)注的解決方案，論文第四章討論了基于概率的分類原理，提出了兩種概率神經(jīng)網(wǎng)絡(luò)脈沖去交錯(cuò)器結(jié)構(gòu)，分別適用于未知輻射源及具有先驗(yàn)信息輻射源兩種情況。
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