基于人工神经元网络法的隧道地表沉降分析及预测

隧道开挖变形控制是隧道施工安全控制的关键环节，但地质条件的多样性、工艺的复杂性使得科学地分析、预测隧道变形变得较为困难。笔者以哈尔滨市保健路隧道工程为依托，基于中导洞特征断面的观测数据，采用人工神经元网络法对特征断面地面沉降随时间及土层物理因素（土层厚度、压缩系数、摩擦角、黏聚力及压缩模量等）的变化进行分析及预测。通过对比理沧预测结果与实际测量结果可知，隧道中导洞地表沉降预测模型误差在6％以内；隧道特征断面地表沉降预测模型误差在4％以内，外延性预测误差在7％以内。

人工神经元网络方法在毛细管电泳和色谱分析中的应用

综述了人工神经元网络方法在毛细管电泳和色谱分析中的应用 ,内容包括迁移 (或保留 )行为的预测 ,分离优化 ,模式识别及分类 ,重叠峰定量解析 ,非线性过程的模型化 ,峰纯度的判断等。还对人工神经元网络在色谱和毛细管电泳中将来可能的应用进行了探讨。引用文献 5 2篇。

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

A forming load analysis for extrusion process of AZ31 magnesium

The effect of extrusion parameters on the extrusion load for AZ31 magnesium alloy was investigated with the support of numerical methods.With this regard,the process temperature,extrusion ratio,friction factor and punch velocity were selected as main parameters for the experiments.Besides,the experimental results were analyzed by using the finite element method(FEM)and artificial neural network(ANN)method to build a numerical model for predicting the forming load.All the experimental and numerical results were compared to each other and it was concluded from the results that the effect of friction factor on the extrusion load is more dominant at lower extrusion temperature for all given extrusion ratios and punch velocities.Besides this,higher extrusion ratios require higher process temperatures to obtain the lower extrusion load.Also,it was observed that the increase in the extrusion speed causes a significant increase in the forming load for all extrusion ratios and extrusion temperatures.

Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.

The applying of BP network in forecasting the demand and its growth rate for coal

Based on the statistical data from 1975 to 1997, we forecast the growth rate of coal consuming and the quantity in coming decade with the BP neuron network in the article.

Retrieval and analysis of sea surface air temperature and relative humidity

Air temperature and relative humidity have been the main parameters of meteorology study. In the past data could be obtained from in-situ observations, but the observations are local and sparse, especially over ocean. Now we can get them from satellites, yet it is hard to estimate them from sat- ellites directly so far. This paper presents a new method to retrieve monthly averaged sea air temper- ature （SAT） and relative humidity （RH） near sea surface from satellite data with artificial neural networks （ANN）. Compared with the observations in Pacific and Atlantic, the root mean square （RMS） and the correlation between the estimated SAT and the observations are about 0.91 ~C and 0.99, respectively. The RMS and the correlation of RH are about 3.73% and 0.65, respectively. Compared with the multiple regression method, the ANN methodology is more powerful in building nonlinear relations in this research. Thus the global monthly average SAT and RH are retrieved from the fixed ANN network from July 1987 to May 2004. In general the annual average SAT shows the increasing trend in recent 18 years. The abnormality of SAT is decomposed with the empirical or- thogonal function （EOF）. The leading three EOFs could explain 84% of the total variation. EOF1 （76.1%） presents the seasonal change of the SAT abnormality. EOF2 （4.6%） is mainly related with ENSO. EOF3 （3.3%） shows some new interesting phenomena appearing in the three main currents in Pacific, Atlantic and Indian Ocean.

Development of a Methodology for Determination and Analysis of Thermal Displacements of Machine Tools Using Finite Elements Method and Artificial Neural Network

In the processes of manufacturing, MT （machine tools） plays an important role in the manufacture of work pieces with complex and high dimensional and geometric accuracy. Much of the errors of a machine tool are those which are thermally induced which are from internal and external heat sources acting on the machine. In this paper, a methodology for determining and analyzing the thermal deformation of machine tools using FEM （finite element method） and ANN （artificial neural networks） is presented. After modeling the machine using FEM is defined the location of the heat sources, it is possible to obtain the temperature gradient and the corresponding thermal deformation at predetermined periods. Results obtained with simulations using the software NX.7.5 showed that this methodology is an effective tool in determining the thermal deformation of the machine, correlating the temperature reading at strategic points with volumetric deformation at the tool tip. Therefore, the thermal analysis of the errors in the pair tool part can be established. After training and validation process, the network will be able to make the prediction of thermal errors just stating the temperature values of specific points of each heat source, providing a way for compensation of thermally induced errors.