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parameter training
相关语句
  参数训练
     The structure of fuzzy logic system predicator based on neural network is proposed. Its parameter training is carried out by using mutation GA algorithm and self-tunning adaptivity aross.
     提出了基于神经网络的模糊逻辑系统辨识器的结构形式,利用自适应交叉率、变异率GA算法对其进行参数训练
短句来源
     In order to decrease the parameter size of acoustic models in speech recognition and improve the robustness of parameter training, a fuzzy clustering analysis method was used for parameter clustering. Based on the structure of phonetic decision tree, the proposed parameter clustering method includes Gaussians clustering and covariance sharing.
     为减少语音识别中声学模型的参数量,提高参数训练的鲁棒性,基于声学决策树结构,提出利用模糊聚类分析方法对模型参数聚类,包括高斯聚类和方差共享.
短句来源
     And a three layer neural network to simulate the most concise rules is constructed. Inputs and outputs of the neural network are decided by the parameter training method that is provided in this paper.
     然后构造三层神经网络模拟最简规则,其中网络的输入输出由本文提出的参数训练方法确定.
短句来源
     To efficiently decrease the parameter size and improve the robustness of parameter training, a revaluing fuzzy-clustering based on Phonetic Tied-mixture HMM (PTM), i.e. FPTM, was presented.
     为减少语音识别中声学模型的参数量,提高参数训练的鲁棒性,提出了一种基于升值法模糊聚类的异音混合共享模型。
短句来源
     The wavelet neural network model is established by combining neural network with the theory of wavelet analysis. The evolutional algorithm is adopted in parameter training.
     将神经网络和小波分析理论相结合建立小波神经网络模型,在网络参数训练中采用进化算法,设计小波神经网络进化算法步骤。
短句来源
  “parameter training”译为未确定词的双语例句
     Although HMT model captures intrascale and interscale dependencies of wavelet coefficients, model parameter training is complex and computationally expensive.
     小波域隐马尔可夫树(HiddenMarkovTree,HMT)模型可以很好地刻画尺度内与尺度间小波系数的相关性,但模型参数的训练过程复杂,计算量大。
短句来源
     The membership function of fuzzy net adopts the triangle function. The parameter training adopts the improved evolutionary programming algorithm with the proposed recursivechanging rate.
     网络模型的隶属度函数采用三角形函数,参数学习采用改进的进化规划算法,并提出递归变化率的概念。
短句来源
     It analyzes keystroke feature extraction, observation encoding and HMM parameter training.
     并对击键特征值的提取、观测值编码等问题进行了分析。
短句来源
  相似匹配句对
     Training
     培训
短句来源
     Parameter Design and Training for Creativity
     参数化设计技术与创新人才的培养
短句来源
     Parameter estimation is the core problem of the training process.
     其中,参数估计是学习问题的核心。
短句来源
     elctronic training
     浅谈电子培训(E-training)
短句来源
     and b was empirical parameter.
     b为经验常数。
短句来源
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  parameter training
The structure/parameter training algorithm exhibits good learning and generalization capabilities as demonstrated via a series of simulation studies.
      
We specifically attempt to incorporate language modelling and parameter training into our algorithm.
      
The integer linear programming technique used in this work, can be applied to parameter training for other problems.
      
Therefore, the more the initial MFs resemble the optimal ones, the easier it will be for the parameter training to converge.
      


An effective network learning method is formed by combining GA,BP algorithms with fuzzy logic system. The structure of fuzzy logic system predicator based on neural network is proposed. Its parameter training is carried out by using mutation GA algorithm and self-tunning adaptivity aross. Computer simulation and practical model predication for hydrocracking have shown the feasibility and practability of this method. This method is expected to provide an effective and helpful means for optimizing operation...

An effective network learning method is formed by combining GA,BP algorithms with fuzzy logic system. The structure of fuzzy logic system predicator based on neural network is proposed. Its parameter training is carried out by using mutation GA algorithm and self-tunning adaptivity aross. Computer simulation and practical model predication for hydrocracking have shown the feasibility and practability of this method. This method is expected to provide an effective and helpful means for optimizing operation and model predication in petrochemical industry.

将模糊逻辑系统、GA算法与BP算法相结合,形成一种有效网络学习方法。提出了基于神经网络的模糊逻辑系统辨识器的结构形式,利用自适应交叉率、变异率GA算法对其进行参数训练。通过计算机仿真和加氢裂化装置航煤干点模型预测,表明该方法的可行性和实用性,可望为石油化工模型预测、优化操作提供一种辅助性有效手段。

In this paper,an intelligent flow prediction and synchronization mechanism(IFSM)is proposed which is composed of a backpropagation neural network (BPNN)traffic predictor,a playout buffer and a fuzzy neural network(FNN)based playout rate determinator The BPNN traffic predictor no line predicts the mean packet rate of the traffic in the future interval (FI)and the FNN is designed to adaptively determinate the playout time according to the number of packets in the buffer and the traffic character predicted Simulation...

In this paper,an intelligent flow prediction and synchronization mechanism(IFSM)is proposed which is composed of a backpropagation neural network (BPNN)traffic predictor,a playout buffer and a fuzzy neural network(FNN)based playout rate determinator The BPNN traffic predictor no line predicts the mean packet rate of the traffic in the future interval (FI)and the FNN is designed to adaptively determinate the playout time according to the number of packets in the buffer and the traffic character predicted Simulation results show that compared to the window mechanism,IFSM achieves high continuity with accepted delay Furthermore,IFSM can be adaptively modified to meet the QoS of different kinds of services by FNN parameter training

本文提出了一种智能化视频流量的预测和同步机制(IFSM),它由BP神经网络流量预测器(BPNN)、输出缓冲区和基于模糊神经网络(FNN)的输出速率决策器所组成。BPNN采用一种在线训练的BP神经网络预测在将来的一定时间间隔(FI)内的平均分组速率,FNN决策器根据预测的流量特性和缓冲区中的分组数动态地调节下一个分组输出的时间。仿真结果表明:与窗口机制相比,IFSM能够使视频流量取得较高的连续性和较低的时延,并且由于FNN的学习能力,IFSM可以自适应地调节相应参数以满足不同的服务质量的要求。

This article proposes a sliding mode surface and a sliding mode controller based on the neural network concept to promote the robustness of systems. Parameter training is done with a fast optimizing algorithm after the reliability of sliding mode condition is tested. The application of this design in the permanent magnet synchronous motor speed regulator shows that it can meet the demands of the systems.

从提高系统的鲁棒性出发 ,提出一种基于神经网络的滑模面和滑模控制器 ,在证明滑模条件成立的基础上 ,采用了快速优化学习算法对其进行参数训练 .经在永磁同步电动机调速系统中应用 ,表明本文提出的设计方法可满足系统要求 .

 
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