Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Optimization of the Conceptual Model of Green-Ampt Using Artificial Neural Network Model (ANN) and WMS to Estimate Infiltration Rate of Soil (Case Study: Kakasharaf Watershed, Khorram Abad, Iran)

Determination of the infiltration rate in a watershed is not easy and in empirical and theoretical point of view, it is important to access average value of infiltration. Infiltration models has main role in managing water sources. Therefore different types of models with various degrees of complexity were developed to reach this aim. Most of the estimating methods of soil infiltration are expensive and time consuming and these methods estimate infiltration with hypothesis of zero slope. One of the conceptual and physical models for estimating soil infiltration is Green-Ampt model which is similar to Richard model. This model uses slope factor in estimating infiltration and this is the power point of Green-Ampt model. In this research the empirical model of Green-Ampt was optimized with integrating artificial neural network model (ANN) and a model of geographical information system WMS to estimate the infiltration in Kakasharaf watershed. Results of the comparison between the output of this method and real value of infiltration in region (through multiple cylinders) showed that this method can estimate the infiltration rate of Kakasharaf watershed with low error and acceptable accuracy (Nash-Sutcliff performance coefficient 0.821, square error 0.216, correlation coefficient 0.905 and model error 0.024).

Evaluation of Intensive Urban Land Use Based on an Artificial Neural Network Model:A Case Study of Nanjing City,China

In this paper, the artificial neural network(ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas.

Artificial neural network modeling of mechanical properties of armor steel under complex loading conditions

An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared with several phenomenological and physically based constitutive models. The ANN model has a much better applicability than the other models in characterization of the flow stress. The tempera- ture and the strain rate effects on the flow stress can be described successfully by the ANN model, with an average error of 1.78% for both quasi-static and dynamic loading conditions. Besides its high accuracy in prediction of the strain rate jump tests, the ANN model is more convenient in model es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.

Analysis of the Mass Appraisal Model by Using Artificial Neural Network in Kaohsiung City

An accurate assessment of the property value is very important to make a deal, property tax, and mortgage for loan. The mass appraisal system has been developed in some foreign countries, especially in American for a long time. In Taiwan, we still have few experiences in using computer-assisted mass appraisal system, especially using artificial neural network （ANN）. This article has two objectives： （1） to illustrate application of ANN to the Kaohsiung property market by the method of back-propagation. The study is based on the properties data of sales price, we also use multiple regressions in the same data; （2） to evaluate the performance of two models by using the mean absolute percentage error （MAPE） and hit ratio （HR）. This paper finds that using artificial neural network （ANN） is able to overcome multiple regressions＇ methodological problems and also get better performance than multiple regression model （MRA）. These results are useful in helping local government to assess their assessment value.

A robust behavior of Feed Forward Back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non- linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks （ANN） perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single- layer feed-forward neural network with the back propagation algorithm is chosen as one of the well- suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken tk^r training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region （78°7＇30＂E and 8°48＇45＂N）, Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding （VES） data, and this trained network is demon- strated by the field data. Groundwater table depth also has been modeled.

Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns

A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—Demographics.The Prey-Predator algorithm is employed for the training.Multilayer perceptron neural network(MLPNN)is used in this study.To improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–PPA.The performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia.The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789.The number of recoveries will be 2000 to 4000 per day.

Neural network-based model for prediction of permanent deformation of unbound granular materials

Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.

Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments

Ionic polymer-metal composites (IPMCs) are especially interesting electroactive polymers because they show large a deformation in the presence of a very low driving voltage (around 1 - 2 V) and several applications have recently been proposed. Normally a humid environment is required for the best operation, although some IPMCs can operate in a dry environment, after proper encapsulation or if a solid electrolyte is used in the manufacturing process. However, such solutions usually lead to increasing mechanical stiffness and to a reduction of actuation capabilities. In this study we focus on the behaviour of non-encapsulated IPMCs as actuators in dry environments, in order to obtain relevant information for design tasks linked to the development of active devices based on this kind of smart material. The non-linear response obtained in the characterisation tests is especially well-suited to modelling these actuators with the help of artificial neural networks (ANNs). Once trained with the help of characterisation data, such neural networks prove to be a precise simulation tool for describing IPMC response in dry environments.

A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran

In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.

基于BP-ANN与RBF-ANN的钢筋与混凝土黏结强度预测模型研究

为研究神经网络对钢筋与混凝土黏结强度的预测能力以及神经网络的输出性能,基于大量的试验数据,提出一种基于改进神经网络的变形钢筋与混凝土黏结强度预测模型,对混凝土结构的研究与实际工程应用均有着重要的意义。收集290组黏结锚固试验数据,引入基于反向传播人工神经网络(BP-ANN)与径向基函数神经网络(RBF-ANN)算法,揭示混凝土强度、保护层厚度、钢筋直径、锚固长度及配箍率对变形钢筋与混凝土黏结性能的影响规律,建立基于改进神经网络算法的钢筋与混凝土黏结强度预测模型。对比分析不同数据预处理方法和训练神经元个数对建议模型预测结果的影响,评估各经典模型与建议模型的预测精度和离散性,提出临界锚固长度计算公式。结果表明:BP-ANN预测值与试验值比值的均值、标准差及变异系数分别为1.009、0.188、0.86,其预测精度略高于RBF-ANN;建议模型能够更准确、更稳定地预测钢筋与混凝土的黏结强度,该方法为解决钢筋与混凝土黏结问题提供了新思路。

The Calculation Model for Operation Cost of Coal Resources Development Based on ANN

On the basis of analysis and selection of factors influencing operation cost of coal resources development, fuzzy set method and artificial neural network (ANN) were adopted to set up the classification analysis model of coal resources. The collected samples were classified by using this model. Meanwhile, the pattern recognition model for classifying of the coal resources was built according to the factors influencing operation cost. Based on the results achieved above, in the light of the theory of information diffusion, the calculation model for operation cost of coal resources development has been presented and applied in practice, showing that these models are reasonable.

Study on Residual Oil HDS Process with Mechanism Model and ANN Model

Based on the Residual Oil Hydrodesulfurization Treatment Unit (S-RHT), the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network (ANN) model were developed to determine the sulfur content of hydrogenated residual oil. The established ANN model covered 4 input variables, 1 output variable and 1 hidden layer with 15 neurons. The comparison between the results of two models was listed. The results showed that the predicted mean relative errors of the two models with three different sample data were less than 5% and both the two models had good predictive precision and extrapolative feature for the HDS process. The mean relative error of 5 sets of testing data of the ANN model was 1.62%—3.23%, all of which were smaller than that of the common mechanism model (3.47%— 4.13%). It showed that the ANN model was better than the mechanism model both in terms of fitting results and fitting difficulty. The models could be easily applied in practice and could also provide a reference for the further research of residual oil HDS process.

Agent Modeling of User Preferences Based on Fuzzy Classified ANNs in Automated Negotiation

In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.

ANN model of subdivision error based on genetic algorithm

According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.

Novel Neural Network Inspired by Neuro-Endocrine-Immune System with Its Application to Beam Pumping Unit

Inspired by the modulation mechanism of neuroendocrine-immune system(NEIs),a novel structure of artificial neural network(ANN) named NEI-NN and its learning method are presented.The NEI-NN includes two parts,i.e.,positive subnetwork(PSN) and negative sub-network(NSN).The neuron functions of PSN and NSN are designed according to the increased and decreased secretion functions of hormone,respectively.In order to make the novel neural network learn quickly,the novel neuron based on some characteristics of NEIs is also redesigned.Besides the normal input signals,two control signals are considered in the proposed solution.One is the enable/disable signal,and the other is the slope control signal.The former can modify the structure of NEI-NN,and the later can regulate the evolutionary speed of NEINN.The NEI-NN can obtain the optimized network structure by using error back-propagation(BP) learning algorithm.Since the modeling of the beam pumping unit is very difficult by using the conventional method,the modeling of bean bump unit is chosen to examine the performance of the NEI-NN.The experiment results show that the optimized structure and learning speed of NEI-NN are better than those of the conventional neural network.

基于ANN与GIS技术的区域岩溶塌陷稳定性预测——以桂林西城区为例

岩溶地面塌陷是岩溶区常见的一种地质灾害,塌陷区域预测是进行国土规划、资源开发与灾害防治的必要工作.由于岩溶塌陷的影响因素众多且相互作用,发展过程复杂,加之各评价因子的数值获取困难,致使长期以来塌陷区域定量预测成为一个难以解决的课题.现行的区域预测模型不能描述塌陷形成模式的非线性特征,也难以克服评价因子权重确定过程中人为经验因素的影响.神经网络技术的自学习、自适应与高度非线性映射特点显示了其在塌陷区域预测领域中应用的前景.根据研究区内地面塌陷空间聚集分布的特征,提出了不同因子组合条件下塌陷发生可能性的定量化方法,结合选定的评价因子类别确定了神经网络预测模型的结构,利用312个塌陷点样本中的292个进行网络训练,余下的20个样本的校验结果表明该模型具有较高的可信度.运用GIS技术将研究区进行评价单元划分,并获取各评价因子的取值,输入到训练好的网络中进行预测.将各单元的输出值进行归并处理后得到研究区岩溶塌陷的稳定级分区图.

土地利用变化的ANN-CA模拟研究--以西南喀斯特地区猫跳河流域为例

为了准确把握西南喀斯特地区土地利用格局的变化规律,以贵州省猫跳河流域为例,采用人工神经网络与元胞自动机的耦合模型对喀斯特地区1990—2002年间的土地利用格局变化进行了模拟。将模拟结果与实际土地利用图进行对比发现:在数量变化方面,两者的混淆矩阵和性能指数的总精度分别达到了87.62%和57.36%;在空间格局方面,模拟结果的景观格局指数均接近真实值。研究结果表明,该模型模拟精度较高且可操作性强,能够作为西南喀斯特地区小尺度范围土地利用变化研究的有效工具。

基于GIS与ANN的土地转化模型在城市空间扩展研究中的应用--以北京市为例

结合GIS强大的空间分析功能与人工神经网络(ANN)处理非线性适应性信息的独特能力建立一种土地转化模型(land transformation model,LTM),用以定量分析城市土地扩展与社会、政策、环境等因子之间关系,并基于此对城市空间扩展的动态进行模拟与预测。LTM模型的运行主要分为3步:因子选取与数据预处理;建立人工神经网络并输入数据对其进行训练与仿真;应用PID法对人工神经网络的输出进行分析,同时在GIS平台上模拟出城市扩展的动态分布。选取相应的影响因子并运用该模型对北京市的城市扩展进行实证模拟检验与预测,结果表明此LTM模型确实提供了一种定量分析和预测城市空间扩展的方法,能够为城市规划与城市发展政策的制定提供重要的科学参考。