登录

双语推荐：神经网络

进入期刊文章
人工神经网络是一种新技术，具有非线性处理、适应学习能力强等优势， BP神经网络是人工神经网络中的一种，具有极强的非线性处理能力。本文就人工神经网络和BP神经网络的相关概念进行分析，研究基于BP神经网络的教学质量评价体系，并对BP神经网络在教学质量评价中的应用进行探讨，以便建立完善的质量评价体系，促进教学质量的提高，推动高职院校的发展。
With the continuous development of science and technology, a new type of teaching quality evaluation system has being generated gradually. Artificial neural network is a new technology, which has such advantages as nonlinear processing and adaptive learning capability. While BP neural network is such a kind of artificial neural network, it has strong ability of nonlinear processing. The related concepts of artificial neural network and BP neural network were analyzed in this paper. With the researching on teaching quality evaluation system based on BP neural network, it discussed the application of BP neural network in teaching quality evaluation. Thus could be helpful to establish perfect quality evaluation system and promote the development of higher vocational colleges.

[ 可能符合您检索需要的词汇 ]

进入期刊文章
在分析凌汛成因的基础上选取合适的预报因子,针对BP神经网络收敛速度慢、易陷入局部极小值的缺点,利用改进的人工鱼群算法训练BP神经网络,以黄河宁蒙河段封开河日期数据进行建模,给出了人工鱼群算法训练神经网络的基本原理和步骤,并对人工鱼群算法神经网络模型、遗传算法神经网络模型、粒子群神经网络模型的预测结果进行了对比分析。结果表明:人工鱼群算法神经网络模型对黄河内蒙古段凌汛期的封开河日期预测比较准确,预测结果优于遗传算法神经网络模型和粒子群神经网络模型。
Based on the analysis on the factors affecting the formation of ice-jam flood,the most important factors for forecasting were selected. Ac-cording to weak points of slow convergence and being apt to local minimum about BP neural network,adopting artificial fish-swarm algorithm was suggested to train the artificial neural network. According to the freeze-up and break-up date of Ningxia-Inner Mongolia section of the Yellow River, neural networks had been trained by adopting AFSA to build an AFSA-NN,which was realized by MATLAB 7. 0 and employed to forecast ice flood. The case study shows that this algorithm forecast is correct for freeze-up and break-up date in the ice flood season in Inner Mongolia reach of the Yellow River. The forecast result is better than GA-BP and PSO-BP neural network.

[ 可能符合您检索需要的词汇 ]

进入期刊文章
BP神经网络能较好处理非线性化数据，但传统BP神经网络存在着局限性，为了提高神经网络运算的精确度，通过权值和学习率共同优化，并采用贝叶斯正则化算法训练神经网络，形成了基于改进型BP神经网络的管理信息系统开发风险评价模型，经测算，该模型输出值与实际值高度吻合，模型可接受度较高，并且与传统BP神经网络相比，改进型BP神经网络的相对误差更小。
BP neural network can deal with nonlinear data rightly,but traditional BP neural network has limitations. In order to boost the precision,a new evaluation model of management information system development risks based on improved BP neural network is established through the co-optimization of weight and learning ratio as well as the application of Bayesian regularization algorithm in training neural network. After calculation,the cutput value of this model accords with the actul value,this model has a high acceptability. Besides,compared with traditional BP neural network,the relative error of improved BP Neural Network is smaller.

[ 可能符合您检索需要的词汇 ]

进入期刊文章
针对中小型污水处理厂出水监测设备长期监测精度不稳定的实际情况,利用灰色理论和广义回归神经网络建立了灰色神经网络模型,根据采集到的污水处理厂进水参数对出水参数进行预测,并对灰色神经网络模型和广义灰色神经网络模型的预测结果进行了对比,对比结果表明:这种灰色神经网络模型的精度明显优于广义神经网络模型,适合应用.
@@@@According to the unstable accuracy of long-term monitoring to the small and medium sewage treatment plant, grey neural network model is established by making use of grey theory and generalized regression neural network. In terms of bilging pa-rameter collected from the sewage treatment plant, we make a prediction to the effluent parameter and make a contrast to the result of the prediction between grey neural network and generalized regression neural network. It turns out that the accuracy of the former is extremely superior to the latter. It is suit for application.

[ 可能符合您检索需要的词汇 ]

进入期刊文章
介绍了BP神经网络和遗传算法的概念和基本理论,详细阐述了遗传算法优化的BP神经网络和BP神经网络这两种神经网络模型,深入分析了两种模型在基坑水平位移监测的数据预报,指出了遗传算法优化的BP神经网络模型具有更好的预测效果.
@@@@The author introduced basic concepts and theories of BP neural network and genetic algorithm. Then, the author minutely expounded two neural network models of BP neural network optimized by genetic algorithm, and deeply analyzed data forecast of two models in monitoring of horizontal displacement of foundation pit. Finally, the author put forward BP neural network models optimized by genetic algorithm had better forecast effect.

[ 可能符合您检索需要的词汇 ]

进入期刊文章
为了研究Gaussian型RBF神经网络的逼近能力,首先介绍了Gaussian型RBF神经网络的结构和算法,然后在MATLAB7.0环境下,编程建立了Gaussian型RBF神经网络和BP神经网络,并以具体的非线性函数为例,分别用两种神经网络对其进行逼近.仿真结果表明,相对于传统BP神经网络而言,Gaussian型RBF神经网络对于非线性函数的逼近精度更高、收敛速度更快,具有良好的逼近能力,为解决非线性函数的逼近问题提供了良好的解决手段.
In this paper,in order to study the approximation ability of Gaussian-RBF neural networks,firstly the structure and algorithm of Gaussian-RBF neural networks are introduced. Secondly Gaussian-RBF neural networks and BP neural networks are designed on MATLAB7.0 platform. Then the two kinds of neural networks are used to approximate a certain nonlinear function. Simulation results show that for nonlinear functions,Gaussian-RBF neural networks are superior to BP neural networks in approximation precision,convergence rate as well as approxi-mation performance. Thus they provide an ideal method for the solution of single-variable nonlinearity function approximation.

[ 可能符合您检索需要的词汇 ]

进入期刊文章
为了提高灰色神经网络在人工智能预测领域中的预测准确性,提出一种改进布谷鸟算法优化灰色神经网络的预测方法.通过改进的布谷鸟算法对常规灰色神经网络(GNN)的白化参数进行优化,寻找出最优初始化参数,并将其结果作为灰色神经网络的输入,建立了基于改进布谷鸟优化的灰色神经网络预测模型,在此基础上,采用该方法对煤与瓦斯突出进行预测.仿真实验表明,该模型的预测精度要优于标准灰色神经网络和基于粒子群算法的灰色神经网络等方法.
In order to improve the prediction accuracy of the Gray Neural Network (GNN)in the field of artificial intelligence prediction,the GNN based on the Improved Cuckoo Search Algorithm(CS-GNN)has been proposed in this paper.To find out the optimal initialization parameters,the conventional GNN whit-ening parameters have been optimized by the improved cuckoo search algorithm,and the results been used as the input of the GNN.A Grey Neural Network model based on improved cuckoo search optimization has been built and used to predict coal and gas outburst.The simulation experiment result indicates that the prediction accuracy of the model is higher than that of the conventional GNN and the GNN based on Particle swarm algorithm (PSO-GNN).

[ 可能符合您检索需要的词汇 ]

进入期刊文章
城市建设用地的准确预测是城市土地总体规划的重要决策基础。通过对影响城市建设用地主要因素的研究，提出一种基于小波神经网络的城市建设用地预测模型，并给出相应的网络学习算法。以湖南省长沙市为例，建立了基于小波神经网络的长沙市建设用地预测模型，比较分析了小波神经网络模型与灰色BP神经网络模型和传统BP神经网络模型的预测结果。分析结果表明：小波神经网络模型比灰色BP神经网络模型和传统BP神经网络模型的收敛速度快、预测精度高，在城市建设用地预测中更具应用价值。该成果为城市建设用地预测研究提供了有益参考。
Accurate forecast of urban construction land (UCL)is an important decision-making basis for general urban land plan.According to the research on principal factors influencing the UCL,we present a WNN-based UCL forecast model,and give its corresponding networks learning algorithm.Changsha city in Hunan province is used as a case,we build a WNN-based UCL forecast model for Changsha,and make the comparison and analyses on the forecast results using WNN model,grey BP neural network and traditional BP neural network model respectively.Analysis result shows that the WNN model has higher convergence speed and forecast precision than the grey BP neural network and the traditional BP neural network model,and has higher applied value in UCL forecasts.Our research work provides a useful reference for the research on UCL forecast.

[ 可能符合您检索需要的词汇 ]

进入期刊文章
Kalman 神经网络以其良好的自适应非线性逼近能力，被广泛用于复杂非线性动态工业过程建模。传统噪声估计方法难以得到观测噪声不确定动态工业过程的噪声估计值，因而常将观测噪声估计值置零以进行 Kalman 神经网络建模，影响Kalman神经网络的建模效果，限制了Kalman神经网络在观测噪声不确定动态工业过程建模中的应用。有效利用观测输入输出数据，提出样本有效噪声估计（Gamma test, GT）改进的Kalman神经网络建模方法。采用衰减记忆的GT对输入输出数据进行实时估计，得到准确的观测噪声估计值，再利用Kalman神经网络实现精确建模。验证结果表明，该方法对EKF神经网络模型和 UKF 神经网络模型均有很好的改善作用，有效解决观测噪声不确定引起的 Kalman 神经网络模型发散问题，为采用Kalman神经网络建立噪声不确定动态工业过程的精确模型提供了一条有效途径。
Kalman filter neural network(KFNN) have been widely used in modeling for complex industrial process, because they have abilities to adaptive approximate the nonlinear and dynamic properties of the process. However, the performances of KFNN will diverge because it can’t get accurate statistics of unmeasurable noise by traditional noise estimation methods. A new KFNN with gamma test(GT) is proposed for industrial process modeling with the unmeasurable noise. The moving window idea is introduced to GT algorithm, and the improved GT is used to track the changes of the observable noise covariance in real time because it can get the accurate statistics of the unmeasurable noise only use the input-output data. Then the covariance in the traditional KFNN is replaced by real-time estimation from the improved GT algorithm. In this way, the KFNN is enhanced by the GT algorithm. In order to verify, the proposed KFNN is used to model the industrial process. The efficiency of the new KFNN

[ 可能符合您检索需要的词汇 ]

进入期刊文章
随着神经网络理论的深入研究，神经网络已在图像的分类中起到非常重要的地位。本文章使用IDL语言来实现神经网络在权值调整过程算法优化，并在ENVI上集成，已达到神经网络分类速度快，且精度可靠的目的。
With the deep research in the theory of neural network, neural network has played a very important role in the classification of the image. This article uses the IDL language to implement the algorithm to optimize the weights of neural network to the adjustment process, and on the ENVI integration, has reached the neural network classification speed, precision and reliable.

[ 可能符合您检索需要的词汇 ]