基于人工神经网络的氮化锆薄膜颜色预测模型

建立了氮化锆薄膜制备工艺参数与薄膜色度参数之间的人工神经网络预测模型，结果表明，预测结果与实测结果吻合，最大色差在5．45以内。利用所建立的模型研究了单个参数对薄膜颜色的影响规律，及多参数间交互作用与薄膜颜色的关系。并且利用神经网络根据加工要求反向预测工艺参数，从而实现了对加工参数的优化选择。     

Modular artificial neural network for prediction of petrophysicalproperties from well log data

An application of Kohonen's self-organizing map (SOM), learning-vector quantization (LVQ) algorithms, and commonly used backpropagation neural network (BPNN) to predict petrophysical properties obtained from well-log data are presented. A modular, artificial neural network (ANN) comprising a complex network made up from a number of subnetworks is introduced. In this approach, the SOM algorithm is applied first to classify the well-log data into a predefined number of classes, This gives an indication of the lithology in the well. The classes obtained from SOM are then appended back to the training input logs for the training of supervised LVQ. After training, LVQ can be used to classify any unknown input logs. A set of BPNN that corresponds to different classes is then trained. Once the network is trained, it is then used as the classification and prediction model for subsequent input data. Results obtained from example studies using the proposed method have shown to be fast and accurate as compared to a single BPNN network     

Air pollution prediction by using an artificial neural network model

Clean Technologies and Environmental Policy - Air pollutants impact public health, socioeconomics, politics, agriculture, and the environment. The objective of this study was to evaluate the...     

Application of artificial neural networks for prediction of the retention indices of alkylbenzenes

Artificial neural networks (ANN) with extended delta–bar–delta (EDBD) learning algorithms were used to predict the retention indices of alkylbenzenes. The data used in this paper include 96 retention indices of 32 alkylbenzenes on three different stationary phases. Four parameters: temperature, boiling point, molar volume and the kind of stationary phase, were used as input parameters. These three stationary phases are: PEG, SE-30, SQ. The 96 group data were randomly divided into two sets: a training set (including 64 group data) and a testing set (including 32 group data). The structures of networks and the learning times were optimized. The best network structure is 4–7–1. The optimum number of learning time is about 20 000. It is shown that the maximum relative error is no more than 3%. The result illustrated that the prediction performance of ANN in the field of investigating the retention behaviors of alkylbenzenes is very satisfactory.     

人工神经网络在材料设计中的应用

在实验数据的基础上,利用人工神经网络建立高Ｃｏ－ Ｎｉ 二次硬化钢的力学性能与合金成分及热处理温度对应关系的模型． 首次提出将五个材料力学性能指标及部分合金成分作为网络的输入,其它合金成分和热处理温度作为网络的输出,根据要求的力学性能设计材料的合金成分含量及热处理条件,获得了满意的结果,为高性能材料设计提供了一定的理论辅助手段．     

人工神经网络建模在抗烧蚀炭/炭复合材料基体改性研究中的应用

基体改件是防止炭／炭复合材料氧化的主要手段。通过将人工神经网络引入炭／炭复合材料的基体改性研究，借助kvenberg-Marquardt算法对不同添加剂组成改性试样所具有的氧化烧蚀率学习，建立了炭／炭复合材料改性添加剂组成一氧化烧蚀率的BP网络模型?研究结果表明：所建模型可以较好地反映添加剂含量与试样氧化烧蚀率间的内在规律，网络模型的输出值和实验验证值间的误差＜0．5％，将模型筛选出的最优配方用于基体改性，试样的氧化烧蚀率下降了49．5％，说明将人工神经网络用于炭／炭复合材料基体改性是可行和有效的。     

三元乙丙橡胶的热老化行为及其BP神经网络预测

通过对三元乙丙橡胶(EPDM)橡胶在70℃、80℃和90℃3个温度下的高温加速老化实验,测试其拉伸强度,断裂伸长率和压缩永久变形的物性值的保持率。建立了EPDM的热老化时间与其3种物性值之间的热老化行为规律关系。结果表明:以3种物性为基础,利用Arrhenius作图法推算出23℃下EPDM橡胶材料的使用寿命分别为316年、158.5年、78.5年。同时,利用BP神经网络预测EPDM橡胶在3个温度下的热老化行为,预测结果误差分别为10-3、10-4、10-5。     

Time-delay neural network for the prediction of carbonation tower's temperature

The carbonation tower is a key reactor to manufacturing synthetic soda ash using the Solvay process. Because of the complexity of the reaction in the tower, it is difficult to control such a nonlinear large-time-delay system with normal measurement instrumentation. To solve this problem, a time-delay neural network (TDNN) is used in the soft measurement model in this paper. A special back-propagation algorithm is developed to train the neural network. Compared with the model based on multilayered perceptron, it is shown that TDNN can describe the system's dynamic character better and predict much more precisely. The influences of the input variables to the output of the model are analyzed with the online data. Analysis results show this model matches the reaction kinetics and the real operating conditions.     

Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

In this study, the effect of aging parameters on wear behavior of PM Inconel 706 (IN 706) superalloy was experimentally investigated and an ANN model was developed to predict weight loss after wear tests. IN 706 superalloy powders were cold pressed (700 MPa) and sintered at 1270 °C for 90 min. The sintered components were gradually aged for 16 h at 730 °C and for 12–20 h at 620 °C. The samples of IN706 superalloy were subjected to wear test at a constant sliding speed of 1 m/s under three different loads (30 N, 45 N and 60 N) and for five different sliding distances (400–2000 m). The results clearly showed that δ, γ′ and γ″ phases were observed around grain boundaries of IN 706 superalloy aged for different periods. The highest hardness was measured for the samples aged for 12 h. Weight losses were found to increase as the sliding distance increased. Moreover, the ANN modeling of weight loss values for IN 706 superalloy gave effective results and can be successfully used to predict weight loss values in the parameters that were determined by the obtained high R² value.     

Application of artificial neural network for predicting plain strain fracture toughness using tensile test results

A back‐propagation neural network was applied to predicting the K_IC values using tensile material data and investigating the effects of crack plane orientation and temperature. The 595 K_IC data of structural steels were used for training and testing the neural network model. In the trained neural network model, yield stress has relatively the most effect on K_IC value among tensile material properties and K_IC value was more sensitive to K_IC test temperature than to crack plane orientation valid in the range of material data covered in this study. The performance of the trained artificial neural network (ANN) was evaluated by comparing output of the ANN with results of a conventional least squares fit to an assumed shape. The conventional linear or nonlinear least squares fitting methods gave very poor fitting results but the results predicted by the trained neural network were considerably satisfactory. This study shows that the neural network can be a good tool to predict K_IC values according to the variation of the temperature and the crack plane orientation using tensile test results.     

Developing a model for hardness prediction in water-quenched and tempered AISI 1045 steel through an artificial neural network

The aim of the current study was to develop an artificial neural network (ANN) model to predict the hardness drop of the water-quenched and tempered AISI 1045 steel specimens, as a function of tempering temperature and time parameters. In the first stage, the effects of selected tempering parameters on the hardness drop value were investigated. In the second stage, a group of data, which have been obtained from experiments, was used for training of the ANN model. Likewise, another group of experimental data was utilized for the ANN model validation. Ultimately, maximum error of the ANN prediction was determined. The agreement between the predicted values of the ANN model with the experimental data was found to be reasonably good.     

基于神经网络趋势分析

文章在分析研究了国内外现状的基础上 ,利用神经网络的非线性处理特性 ,提出了通过神经网络预测常见机械零件剩余寿命的方法 ,用实例验证了其有效性     

Evolutionary neural network modeling for software cumulative failure time prediction

An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg–Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches.     

用人工神经网络预测冰蓄冷系统蓄冰时间

蓄冰时间的预测对于冰蓄冷空调系统的设计和运行控制十分重要。在本文中,作者以理论计算数据作训练集、验证集、测试集,采用BP型人工神经网络预测了板单元冰蓄冷系统的蓄冰时间．取得了令人满意的结果。与采用差分数值计算相比,用神经网络可大大缩短计算时间。     

Modelling urban air quality using artificial neural network

This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been used in the models formulation. Three scenarios were considered—considering both meteorological and traffic characteristics input parameters; only meteorological inputs; and only traffic characteristics input data. The performance of all the developed models was evaluated on the basis of index of agreement (d) and other statistical parameters, viz. the mean and the deviations of the observed and predicted concentrations, mean bias error, mean square error, systematic and unsystematic root mean square error, coefficient of determination and linear best fit constant and gradient (Willmott in B Am Meteorol Soc 63:1309, 1982). The forecast performance of the developed models, with meteorological and traffic characteristics (d=0.78 for AQCR1 and d=0.69 for AQCR2) and with only meteorological inputs (d=0.77 for AQCR1 and d=0.67 for AQCR2), were comparable with the measured data.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

An artificial neural network model for virtual Superpave asphalt mixture design

This study presents an artificial neural network (ANN) model to predict the asphalt mixture volumetrics at Superpave gyration levels. The input data-set needed by the algorithm is composed of gradation of the mix, bulk specific gravity of aggregates, low- and high-performance grade of the binder, binder content of the mix and the target number of gyrations (i.e. N_ini, N_des and N_max). The proposed ANN model uses a three-layer scaled conjugate gradient back-propagation (feed-forward) network. The ANN was trained using data obtained from numerous roads with a total of 1817 different mix designs. Results revealed that the ANN was able to predict V_a within V_a (measured) ± 1.0% range 85–93% of the time and within V_a (measured) ± 0.5% range 60–70% of the time. Currently with the developed ANN model, Superpave mix design can take approximately between 1.5 and 4.5 days, which corresponds to 3–6 days of savings.     

基于人工神经网络的异步电动机故障检测

为进一步提高异步电动机故障检测的准确性,将人工神经网络应用于异步电动机故障检测.通过提出一种基于BP神经网络的电机故障检测方法,设计了适合该检测系统的网络结构.仿真结果表明:相对于其他算法,该网络结构具有更快的学习速度和更高的学习精度,完全适用于电动机故障检测.     

Modeling of rotary vane compressor applying artificial neural network

The thermal modeling of rotary vane compressor (RVC) was performed in this paper by applying Artificial Neural Network (ANN) method. In the first step, appropriate tests were designed and experimental data were collected during steady state operating condition of RVC in the experimental setup. Then parameters including refrigerant suction temperature and pressure, compressor rotating speed as well as refrigerant discharge pressure were adjusted.With these input values, the operating output parameters such as refrigerant mass flow rate and refrigerant discharge temperature were measured. In the second step, the experimental results were used to train ANN model for predicting RVC operating parameters such as refrigerant mass flow rate and compressor power consumption. These predicted operating parameters by ANN model agreed well with the experimental values with correlation coefficient in the range of 0.962-0.998, mean relative errors in the range of 2.79-7.36% as well as root mean square error (RMSE) 10.59 kg h⁻¹ and 12 K for refrigerant mass flow rate and refrigerant discharge temperature, respectively. Results showed closer predictions with experimental results for ANN model in comparison with nolinear regression model.