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    THE INVESTIGATION OF NETWORK STRUCTURE OF A_(a_i)-B_(b_j) TYPE POLYCONDEN SATE Ⅱ. THE CALCULATION OF NETWORK STRUCTURE PARAMETERS
    关于A_(a_i)-B_(b_j)型缩聚物网络结构的研究 Ⅱ.网络结构参数的计算
    Study of the Effect of the Parameters of Neural Network Structure on the Convergence Speed
    神经网络结构参数对收敛速度的影响研究
    The Curing Theory of A f Type Free Radical Homopolymerization(Ⅱ) The Structure Parameters of Polymer Network
    A_f型自由基均聚反应的固化理论(Ⅱ)──凝胶网络的结构参数
    Effect of Disturbances in Transmission Fibers on Structure Parameters of the Optical Fiber Compensation Network
    传输扰动对光纤补偿网络结构参数的影响
    A Method for Structural Parameters Identification Using Neural Networks
    一种基于神经网络的结构参数识别方法
    Network structure parameters of A_f-B_g type nonlinear free radical alternating copolymerization
    非线性A_f-B_g型自由基交替共聚反应的凝胶网络结构参数
    Design and Selection of Construction, Parameters and Training Method of BP Network
    BP网络结构、参数及训练方法的设计与选择
    Determining Automatically Structure Parameters of BP Artificial Neu ral Network by a Computer
    BP神经网络结构参数的计算机自动确定
    STUDIES ON NETWORK PARAMETERS OF NOVEL P(HEMA-co-EMA) COPOLYMERIC HYDROGELS
    新型共聚水凝胶P(HEMA-co-EMA)交联网络的结构参数
    Statistical Parameters of Hydrogen Bonding Networks Formed in Solution of _f-A_aD_d Type
    _f-A_aD_d型氢键体系的网络结构参数
    Structural Parameters for Hydrogen Bonding Networks Formed in Systems of _(a_1)_(d_1)-A_(a_2)D_(d_2) Type
    _(a_1)_(d_1)-A_(a_2)D_(d_2)型氢键体系的网络结构参数
    Statistical Parameters of the Hydrogen Bonded Network
    氢键凝胶网络的结构参数研究
    If the constant k is pre-determined, the heteregeneity factor Z of the inhomogeneous real networks could be evaluated fromZ = k(Q -1)(Q0-1)/[(1-k)(Q-1)-(Q0 - 1)]The proposed method was sucessfully applied o the characterization of the inhomogeneous network of divinylbenzene-styrene random copo ymer initiated by free radicals.
    当k已知时,测定非均一网络在良、劣两种溶剂中溶胀比Q~*和Q_0~*,依 Z=k(Q~*-1)(Q_0~*-1)/[(1-k)(Q~*-1)-(Q_0~*-1)],可得非均一因子Z及其他网络结构参数.
    In this paper the structural control theory of electric power networks and analysis of critical energy are employed to present control strategy for networks structural parameters and evaluation method to improve power system transient stability.
    通过电力网络结构控制及临界能量分析,提出网络结构参数控制对策及评价方法,借以提高系统的暂态稳定性。
    By substituting K + ions in glass all or partially with Rb +, Cs +, adjusting glass network structure parameters and the moiat ratio of PbO and Bi 2O 3, the secondary electron emission coefficint of glass is inereased from 2 7 to 3 7. and the gain stability and lifetime of MCP are improved.
    通过将R+b、C+s离子部分或全部代替玻璃中的K+离子,调整玻璃网络结构参数以及PbO与Bi2O3的摩尔比例,使玻璃的二次电子发射系数从27增加到37,改善微通道板的增益稳定性和寿命
    The new network system functions the optimization seeking for parameters of the genetic algorithm performance, network structure and network performance and revision of the network value with GA-BP algorithm.
    该网络系统具有遗传算法性能参数优选、网络结构参数优选、网络性能参数优选以及GA—BP算法联合进行网络权值修改几种功能。
    Training it with the structure parameters, links reliabilities and exact reliabilities of serveral n node network models, the neural network can learn the relationship between structural parameters, links reliability and reliability of networks.
    用节点数为 n的网络系统的结构参数、网络中边的可靠度以及网络可靠性的精确值对神经网络进行训练 ,使神经网络学习到网络结构参数、网络中边可靠度与网络可靠性之间的映射关系 .
    Using the capability of parallel search with genetic algorithm (GA) dynamically optimizes the structure parameters of BP network.
    利用遗传算法(GA)的并行搜索能力对BP网络结构参数进行动态优化。
    A prediction model based on wavelet neural network for the time series of dynamic errors is established to correct the measurement errors and enhance the dynamic measuring accuracy based on the modern errors correction technique. The wavelet neural network is used as a substitute for the traditional neural network to avoid the limitations that the structure parameters of the network need to be changed artificaially.
    基于现代误差修正技术,研究小波神经网络建立的动态测量误差预测模型,以进行误差修正,提高动态测量精度,避免了传统神经网络需要人为干预网络结构参数的不足。
    The paper advanced a new way of fusion modeling by means of the use of mutative scale chaos optimization method and BP networks to overcome problems in makeup parameters,convergence speed and local minima of BP networks.
    针对 BP神经网络存在的网络结构参数、收敛速度、局部极小等问题 ,提出基于变尺度混沌遗传算法 ( MSCGA)与 BP神经网络相互结合的混合建模新方法 ,可对 BP网络隐层节点数、网络权值、阈值等结构参数进行快速优化设计。
 

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