应用实例验证了所提出的递归神经网络预测模型的有效性。
The presented prediction approach is proved to be useful and effective with simulation resu...
并通过具体的实验验证了改进BP神经网络预测模型的有效性。
The validity of the improved BP neural network predictive model was validated through the experiments.
其次，建立了受腐蚀钢筋混凝土极限粘结力的神经网络预测模型。
Secondly, Neural Network model was built up to predict ultimate bond strength between the rebar and corroded concrete.
其次，建立了受腐蚀钢筋混凝土极限粘结力的神经网络预测模型。
Secondly, Neural Network (NN) model was built up to predict ultimate bond strength between the rebar and corroded concrete.
利用BP神经网络预测模型融合正交实验法对化学镀工艺进行优化。
The technology optimization of electroless plating can be predicted by BP neural network and orthogonal experiment.
实验结果表明，神经网络预测模型具有较佳的学习能力和泛化能力。
Experiment result shows that ANN prediction model is characterized by better learning capacity and generalization capacity.
第三部分对基于混沌优化算法的模糊优选神经网络预测模型进行研究。
In the third part, the prediction model of fuzzy optimal selection neural network based on chaotic optimization algorithm is studied.
采用粒子群算法对镗孔加工尺寸误差人工神经网络预测模型进行优化。
This paper presented particle swarm optimization (PSO) technique to train multi layer artificial neural network for predicting model of diameter errors of boring processes.
与已有神经网络预测模型相比，具有更高的一步预测和多步预测精度。
Comparison with the ANN model shows that both one step forecasting accuracy and multistep forecasting accuracy by the online model are higher than that by ANN model.
同时，分别建立了回弹量的线性回归预测模型和BP神经网络预测模型。
Meanwhile, a linear regression model and a BP neural network model for predicting the springback quantity were set up.
采用BP神经网络预测结点的负载变化情况，并建立BP神经网络预测模型。
Predicting the node's load information changes using the BP neural network, and establishing the prediction model based on BP neural network.
本文建立了基于模糊粗糙集的神经网络预测模型，对瓦斯涌出量进行了预测。
A novel artificial network model based on fuzzy-rough set for gas emission forecasting of coal is proposed in the paper.
采用灰色理论中的等维新息思想构建训练样本，建立了等维新息神经网络预测模型。
A new neural network model is established based on the concept of equal dimension and new information in grey theory.
最后，对两种预测模型的结果进行了对比，验证并联型灰色神经网络预测模型的可行性。
Finally, the forecast results are compared between the two kinds of models. The comparison results indicate the feasibility of PGNN model.
利用人工神经网络方法研究了该工艺的参数协调和变形力预测等问题，建立了神经网络预测模型。
The method of artificial neural network was utilized to research the coordinating of the process parameters and the prediction of the forming pressure, and the model was established.
通过计算机实验，讨论样本、学习算法和网络结构等对神经网络预测模型性能的影响及其改进措施。
Through computer simulation, samples, BP algorithms and the influence of network structure neurula on model performance have been discussed as well as the improving measures.
仿真与实验结果表明，神经网络预测模型与静态方程相结合的控制方法满足温室环境温度控制需求。
The result of experiment and simulation indicates the controlling method using ANN prediction model combined with static equation meets the requirement of controlling greenhouse environment.
为了实现神经网络预测模型的鲁棒预测，提出一种基于非线性偏自相关的一般化预测模型辨识方法。
To solve the problem of the robust prediction of neural networks, the paper proposed a universal method of nonlinear model identification.
最后，以玛纳斯河肯斯瓦特站历年的年径流资料验证时间序列人工神经网络预测模型的可行性与有效性。
Lastly, the feasibility and validity of the model was validated with the past years surface water resource quantity time series data from Kenswat Station on Xinjiang Manas River.
采用单神经元PI控制算法与神经网络预测模型相结合的控制策略，用PI控制规律来确定控制器的输出。
The control strategy unites single neuron PI control algorithm and the predictive model based on neural network. The output of the controller is determined by PI algorithm.
通过检验发现神经网络预测模型在预测精度上要高于随机游动模型，而且两个模型的预测结果存在明显的差异。
Through the test we found that the forecast accuracy of neural network forecasting model is higher than that of random walk model, and there is an obvious difference between two models.
从煤与瓦斯突出的机理出发，构建了多层BP神经网络预测模型，将煤与瓦斯突出的综合影响因素作为特征向量。
Based on the mechanism of coal and gas outburst, we construct the prediction model of BP neuro-network and make the composite factor of the coal and gas outburst as the feature vector.
本文综合考虑水源、水压、隔水层、断层等因素对煤层底板突水的影响，建立了煤层底板突水人工神经网络预测模型。
This paper considers comprehensively water source, water pressure, impedance water strata, and fault ectal factors to establish a ANN forecast model for water-inrush from coal floor.
结合遗传算法(GA)和误差反馈型神经网络(BP)，建立了优化的GA - BP神经网络预测模型，预测转炉炼钢过程钢液终点磷含量。
Combined Genetic Algorithms (ga) and back-propagation neural network (BP), an optimized GA-BP model was established to predict phosphorus content. Some data were chosen to train the network model.
实践表明：两种模型对毛条的豪特长度、长度变异、短纤维含量和精梳落毛率均能进行较为准确的预测，其中人工神经网络预测模型预测效果优于统计回归模型。
Application experience has been shown that both methods have good prediction performance in which the ANN method has better prediction performance in comparison with multiple regression method.
用神经网络建立土壤水分预测模型的方法是可行的。
The method of building soil water forecast model with neural network is available.
讨论、比较了基于神经网络和基于时间序列的预测模型。
It discusses and compares the forecasting models using neural networks and using time series.
结果：对遗传神经网络模型所预测的最佳分离条件进行实验，获得了比较满意的分离结果。
Results: Through the experiment with the optimized separation predicted by Genetic Neural Networks model, satisfied results of separation can be obtained.
结果：对遗传神经网络模型所预测的最佳分离条件进行实验，获得了比较满意的分离结果。
Results: Through the experiment with the optimized separation predicted by Genetic Neural Networks model, satisfied results of separation can be obtained.
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