该模型无需事先确定模糊控制规则，并能通过神经网络的结构及参数学习调整模糊神经网络的结构。
By using this model, people need not select any fuzzy logic in advance, and can adjust the network structure by the structure and parameter learning of the neural network.
针对峰值法中测量路径规划问题，提出了基于参数模型学习的神经网络预测器。
Aiming at measuring path planning by using peak value, neural network predictor based on parameter model is presented.
仿真结果表明：该PSO优化SVR参数方法可行、有效，由此得到的SVR模型具有更好的学习精度和推广能力。
Simulation results show that the optimal selection approach based on PSO is available and the PSO-SVR model has superior learning accuracy and generalization performance.
理论分析说明这种模糊规则后件参数学习算法是收敛的、所建模糊模型能够以要求的精度逼近已知的实验数据。
The learning algorithm and the characteristics of the fuzzy rules model which can approximate the experiment data are shown to converge to any arbitrary accuracy by the theoretical analysis.
介绍了余热处理计算机控制系统的在线温度预报模型、设定模型和参数跟踪自学习模型的建立方法。
The method of establishing on-line temperature prediction model, set point model and parameter tracing self-learning model of computerized heat recovery processing control system is introduced.
贝叶斯网络的学习是数据挖掘中非常重要的一个环节，是将先验知识和模型评价融入训练数据，获得数据中隐藏的拓扑结构和参数的过程。
The learning of Bayesian Networks is an important tache, which combines training data with prior knowledge and model evaluation to acquire the structure hidden in data and parameters.
首先，利用在线模糊竞争学习方法划分输入变量的模糊输入空间，然后利用卡尔曼滤波算法估计模糊模型的参数。
First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm.
以生产数据为学习样本，建立BP神经网络模型，实现对剪切旋压工艺参数的预测。
BP neutral networks model has been established in order to predict process parameter of shear spinning based on many data accumulated during manufacturing.
提出了对随机事件概率分布参数进行自学习的方法，把知识化制造单元中的不确定因素纳入任务控制的数学模型。
The uncertain factors of the knowledgeable manufacturing cell were included in the task control model by utilizing a self-study method of probability distribution parameters of stochastic events.
把1975到2006年全国的客运量数据和其他相关指标作为学习样本，验证寻优参数得到训练模型预测结果的可靠性。
Taking the 1975-2006 National passenger traffic data and other related indicators as a learning sample, then verify the validity of the training model after Parameter Optimization.
利用人工神经网络的方法实现系统云灰色模型的参数白化，提出了系统云灰色神经网络模型SCGNNM(1,1)，并给出了相应的学习算法。
By using neural networks as the approach for whitening system cloud gray model, the system cloud gray neural network models SCGNNM (1, 1), were proposed in this paper.
对贝叶斯网络的参数学习进行了探讨，结合实例统计和相关性分析建立了车身偏差诊断的贝叶斯网络模型。
Parameter study of Bayesian network is investigated. According to the methods of example statistics and correlation analysis, Bayesian diagnosis model of body deviation is established.
该控制器用具有改进学习算法的神经网络作pid参数调节器，用模糊神经网络对被控对象进行模型辨识。
In this controller, an improved study algorithm is adopted as the PID parameter regulator, and a fuzzy network is employed to identify the controlled objects.
通过计算比较，第三种参数作为输出节点的液体空气汽液平衡计算模型，不仅具有良好的学习能力，而且预测结果也比较满意。
The results show that the better recalling ability and satisfied predictions can be obtained with the model for liquid air developed in this paper.
采用人工神经网络的BP算法，以电火花微小孔加工工艺参数正交实验的结果作为神经网络的学习样本，建立电火花微小孔加工多目标工艺参数的预测模型。
Though choosing the experimental results as the learning sample, the performance predictive model of EDM micro-and-small holes is proposed, with the BP algorithm of artificial neural network.
提出了一个基于混合智能的电火花加工电参数学习模型，它模仿熟练操作者的决策过程，由工艺数据库、加工规则库、学习模块和推理模块组成。
A learning model with hybrid intelligence for the electrical parameter in EDM which imitates a decision making process of a skilled operator was described.
通过对泛函网络的分析，提出了一种序列泛函网络模型及学习算法，而网络的泛函参数利用梯度下降法来进行学习。
In this paper, by analyzing the functional network, a new model and learning algorithm of the serial functional networks is proposed.
针对这样的问题，本文提出一种证据丢失参数模型，并推导出包含学习率的EM更新算法。
Aiming at this question, this paper proposes a parameter model under evidence loss and deduce an EM updating algorithm which contains learning rate.
神经网络模型的学习参数为0.01，网络训练迭代次数为500。
The learning parameter is set as 0.01 and the training iteration is taken as 500.
将基于MGS算法的NARMAX模型结构与参数辨识的一体化算法和小波网络相结合，提出一种改进的小波网络学习算法。
Combining wavelet networks with a kind method of model structure design and parameter estimation of NARMAX model, a modified wavelet network training scheme is proposed.
通过迭代学习的方法在大样本下进一步训练这些隐马尔可夫模型参数；
The HMMs' parameters are further trained by the method of iterative learning from a large data set;
在定义了数据结构和变量数组的基础上，给出了参数自学习过程算法，改善了模型样机对不同规格样本工件的适应性。
A parameter self-learning algorithm is presented after defining data structure and variable array to improve the prototype's adaptability to different size of workpieces.
论文的主体部分以内层模型作为系统框架深入分析了小组的学习环境和学习模式等系统参数。
The main part of this issue analyzes the system parameters such as the learning environment and learning model parameters according to the inner model as system framework.
讨论了模型的学习样本、网络参数对预测精度的影响，选出最佳网络参数配置。
The affection of learning samples and network parameters on prediction accuracy was discussed, the best network parameters were selected.
为了减少车辆通过路口的延误，采用云模型建立控制策略，运用Q -学习改进控制模型的参数。
In order to reduce the delay of cars passing through intersections, control strategies are set up by cloud model and some parameters of the control model are improved by Q-learning method.
为了减少车辆通过路口的延误，采用云模型建立控制策略，运用Q -学习改进控制模型的参数。
In order to reduce the delay of cars passing through intersections, control strategies are set up by cloud model and some parameters of the control model are improved by Q-learning method.
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