Adaptive neuro-fuzzy inference system–based model for elevation–surface area–storage interrelationships

In the developing of an optimal operation schedule for dams and reservoirs, reservoir simulation is one of the critical steps that must be taken into consideration. For reservoirs to have more reliable and flexible optimization models, their simulations must be very accurate. However, a major problem with this simulation is the phenomenon of nonlinearity relationships that exist between some parameters of the reservoir. Some of the conventional methods use a linear approach in solving such problems thereby obtaining not very accurate simulation most especially at extreme values, and this greatly influences the efficiency of the model. One method that has been identified as a possible replacement for ANN and other common regression models currently in use for most analysis involving nonlinear cases in hydrology and water resources–related problems is the adaptive neuro-fuzzy inference system (ANFIS). The use of this method and two other different approaches of the ANN method, namely feedforward back-propagation neural network and radial basis function neural network, were adopted in the current study for the simulation of the relationships that exist between elevation, surface area and storage capacity at Langat reservoir system, Malaysia. Also, another model, auto regression (AR), was developed to compare the analysis of the proposed ANFIS and ANN models. The major revelation from this study is that the use of the proposed ANFIS model would ensure a more accurate simulation than the ANN and the classical AR models. The results obtained showed that the simulations obtained through ANFIS were actually more accurate than those of ANN and AR; it is thus concluded that the use of ANFIS method for simulation of reservoir behavior will give better predictions than the use of any new or existing regression models.     

A new methodology for optimization and prediction of rate of penetration during drilling operations

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.     

Efficient prediction of exchange rates with low complexity artificial neural network models

In recent years forecasting of financial data such as interest rate, exchange rate, stock market and bankruptcy has been observed to be a potential field of research due to its importance in financial and managerial decision making. Survey of existing literature reveals that there is a need to develop efficient forecasting models involving less computational load and fast forecasting capability. The present paper aims to fulfill this objective by developing two novel ANN models involving nonlinear inputs and simple ANN structure with one or two neurons. These are: functional link artificial neural network (FLANN) and cascaded functional link artificial neural network (CFLANN). These have been employed to predict currency exchange rate between US$ to British Pound, Indian Rupees and Japanese Yen. The performance of the proposed models have been evaluated through simulation and have been compared with those obtained from standard LMS based forecasting model. It is observed that the CFLANN model performs the best followed by the FLANN and the LMS models.     

Emergence of adaptive behaviors for artificial creature using a combined artificial neural network

This study proposes a method to acquire adaptive behavior for artificial creature which has a lot of joints using a combined Artificial Neural Network (ANN). Experiment in this study focuses on artificial fish model, which has a lot of joints, tracking towards a target in the virtual water environment. In order to control motions of joints, a combined ANN is implemented with the model. At first, one ANN is prepared to control specific joints so as to swim basically in response to minimal input information using evolutionary computation in preliminary experiments. And an new network is constructed by combining its network and the other network. In order to acquire complicate behavior for artificial creature, weights of combined ANN are optimized. Experiment result shows the model which has many joints acquire adaptive swimming behavior towards a target by optimizing combined network.     

Prediction of power output of a coal-fired power plant by artificial neural network

Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network (ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant. The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and observed results are reported.     

人工神经网络在材料设计中的应用及其若干共性问题的研究

应用人工神经网络进行材料设计已取得了可喜的进展，本文回顾了人工神经网络应用于材料制备工艺研究时的一些共性问题，如：样本筛选、网络结构的确定、加快网络训练速度、改善网络预测效果、最优工艺参数的确定等，并给出相应的解决办法。     

Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China)

Infectious diarrhea is an important public health problem around the world. Meteorological factors have been strongly linked to the incidence of infectious diarrhea. Therefore, accurately forecast the number of infectious diarrhea under the effect of meteorological factors is critical to control efforts. In recent decades, development of artificial neural network (ANN) models, as predictors for infectious diseases, have created a great change in infectious disease predictions. In this paper, a three layered feed-forward back-propagation ANN (BPNN) model trained by Levenberg–Marquardt algorithm was developed to predict the weekly number of infectious diarrhea by using meteorological factors as input variable. The meteorological factors were chosen based on the strongly relativity with infectious diarrhea. Also, as a comparison study, the support vector regression (SVR), random forests regression (RFR) and multivariate linear regression (MLR) also were applied as prediction models using the same dataset in addition to BPNN model. The 5-fold cross validation technique was used to avoid the problem of overfitting in models training period. Further, since one of the drawbacks of ANN models is the interpretation of the final model in terms of the relative importance of input variables, a sensitivity analysis is performed to determine the parametric influence on the model outputs. The simulation results obtained from the BPNN confirms the feasibility of this model in terms of applicability and shows better agreement with the actual data, compared to those from the SVR, RFR and MLR models. The BPNN model, described in this paper, is an efficient quantitative tool to evaluate and predict the infectious diarrhea using meteorological factors.     

Customer churn prediction by hybrid neural networks

As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN + ANN) and SOM combined with ANN (SOM + ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN + ANN hybrid model significantly performs better than the SOM + ANN hybrid model and the ANN baseline model.     

The scope of artificial neural network metamodels for precision casting process planning

Precision investment casting process planning has been tackled in the past according to experience. Recently, casting simulation software is being increasingly used to predict product quality by implementing ‘what-if’ scenarios. Input parameters include relatively simple factors such as mould temperature, melting temperature, casting material. They also include factors whose influence is more complex to quantify, such number and location of feeding points, diameter and length of inflow channels, angle of channel with respect to the main sprue axis. Simulation results cannot help the engineer for workpieces other than the one simulated. In this paper a series of feedforward artificial neural network (ANN) models is presented aiming at such generalisation. To achieve this, a large number of software simulation runs were conducted for a number of different small parts, with varying runner geometry and casting conditions. The parameters characterising part geometry have been chosen to be surface area and volume-to-area ratio. The different ANN models predictive capabilities are reflected to the respective training and generalisation errors. A user-friendly interface has been conducted for model execution in a complete application, whose main virtue is expandability.     

A novel hybrid data-driven model for multi-input single-output system simulation

Artificial neural network (ANN)-based data-driven model is an effective and robust tool for multi-input single-output (MISO) system simulation task. However, there are several conundrums which deteriorate the performance of the ANN model. These problems include the hard task of topology design, parameter training, and the balance between simulation accuracy and generalization capability. In order to overcome conundrums mentioned above, a novel hybrid data-driven model named KEK was proposed in this paper. The KEK model was developed by coupling the K-means method for input clustering, ensemble back-propagation (BP) ANN for output estimation, and K-nearest neighbor (KNN) method for output error estimation. A novel calibration method was also proposed for the automatic and global calibration of the KEK model. For the purpose of intercomparison of model performance, the ANN model, KNN model, and proposed KEK model were applied for two applications including the Peak benchmark function simulation and the real-world electricity system daily total load forecasting. The testing results indicated that the KEK model outperformed other two models and showed very good simulation accuracy and generalization capability in the MISO system simulation tasks.

     

An artificial neural-network model for impact properties in X70 pipeline steels

An artificial neural-network (ANN) model has been developed for the analysis and simulation of the correlation between the mechanical properties and composition and thermomechanical treatment parameters of high strength, low alloy steels. The input parameters of the model consist of alloy compositions (C, Si, Mn, P, S, Cu, Ni, Cr, Mo, Ti, V, Nb, Ca, Al, B) and tensile test results (yield strength, ultimate tensile strength, percentage elongation). The outputs of the ANN model include impact energy (?10 °C). The model can be used to calculate the properties of low alloy steels as a function of alloy composition and thermomechanical treatment variables. The current study achieved a good performance of the ANN model, and the results are in agreement with experimental knowledge.     

空间绳系机器人目标抓捕鲁棒自适应控制器设计

针对空间绳系机器人（Tethered space robot,TSR）目标抓捕过程中的稳定控制问题,建立空间绳系机器人系统模型,根据阻抗控制原理,设计基于位置的阻抗控制方法;针对空间绳系机器人系统的模型不确定性问题,利用神经网络对不确定性进行估计补偿,设计鲁棒项对空间系绳干扰和神经网络估计误差的影响进行抑制,在此基础上设计空间绳系机器人目标抓捕鲁棒自适应稳定控制器,并进行稳定性证明.最后对设计的控制器进行仿真验证.作为对比,对无鲁棒项自适应的稳定控制器进行仿真.仿真结果表明,设计的基于阻抗控制的鲁棒自适应控制可以实现对空间绳系机器人目标抓捕过程中的稳定控制,与无鲁棒项自适应的稳定控制器仿真结果相比,本文采用的鲁棒自适应控制方法可以有效地对不确定性进行补偿,控制过程中超调量更小,收敛时间更短,并且控制精度更高.     

The asymptotic optimization of pre-edited ANN classifier

The generalization problem of an artificial neural network (ANN) classifier with unlimited size of training sample, namely asymptotic optimization in probability, is discussed in this paper. As an improved ANN network model, the pre-edited ANN classifier shows better practical performance than the standard one. However, it has not been widely applied due to the absence of the related theoretical support. To further promote its application in practice, the asymptotic optimization of the pre-edited ANN classifier is studied in this paper. To help study ANN asymptotic optimization in probability, we gives a review of the previous research works on asymptotic optimization in probability of non-parametric classifier, and grouped the main methods into four classes: two-step method, one-step method, generalization method and hypothesis method. In this paper, we adopt generalization/hypothesis mixed method to prove that pre-edited ANN is asymptotically optimal in probability. Furthermore, a simulation is presented to provide an experimental support for our theoretical work.     

Online tool wear prediction in drilling operations using selective artificial neural network ensemble model

Online tool wear prediction plays a key role in industry automation for higher productivity and product quality. In recent past, several artificial neural network (ANN) models using multiple sensor signals as inputs for prediction as well as classification of tool wear have been proposed. However, a single ANN used in these models is often tries, which could limit their wide applications due to the complicated procedure of constructing a single ANN model. This study proposed a selective ANN ensemble approach DPSOEN, where several selected component ANNs are jointly used to online predict flank wear in drilling operation. DPSOEN provides more simple training and better generalization performance than using single ANN and hence is easier to be used by operators who often are not good at ANN techniques. Two benchmark cases were used to evaluate the performance of DPSOEN in predicting flank wear. It shows improved generalization performance that outperforms those of single ANN and Ensemble ALL approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to predict tool wear online with potential applications for tool condition monitoring in general. Analysis from this study provides guidelines in developing ANN ensemble-based tool wear prediction systems.     

The prediction of SO2 removal using statistical methods and artificial neural network

Artificial neural network and a statistical model have been applied in a laboratory scale trickle bed reactor (TBR) to investigate the SO₂ removal efficiency of activated carbon. The performance of artificial neural network (ANN) model has been compared with the statistical model based on central composite experimental design. Two independent variables, which affect the amount of SO₂ removal by the liquid phase in the TBR, were selected; namely liquid flow rate and gas flow rate. Amount of SO₂ removal was chosen as the dependent variable (target data). A second order statistical model has been considered to show the dependence of the amount of SO₂ removal on the operating parameters. A back-propagation ANN has been used to develop a model relating to the amount of SO₂ removal. A series of experiments have been conducted on the basis of the statistics-based design of experimental method. It is observed that a neural network architecture having one input layer with two neurons, one hidden layer with three neurons, one output layer with one neuron and an epoch size of 20 gives better prediction. The predictions are more accurate than those obtained from regression models.     

Analysis of a variable speed vapor compression system using artificial neural networks

An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a system.An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port system with inputs and outputs. Therefore, a vapor compression system can be modeled using an ANN in a mapping configuration. That is, some parameters from the compression system can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting.This paper presents a new method to model a variable speed vapor compression system. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a vapor compression system, concluding that the model can predict the energetic performance of a variable speed vapor compression system. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the vapor compression system.     

霍尔电压传感器纹波效应及非线性误差的综合校正

某些霍尔电压传感器在测试含有高频谐波的直流电压量时,存在着由纹波效应等原因引起的严重非线性误差问题,本文提出一种基于神经网络信息融合技术的传感器误差综合校正法.该法直接从传感器输出信号中提取纹波电压特征量,不需要附加检测传感器.将纹波电压作为非目标参量,输入电压作为目标参量,理想输出作为目标值,通过神经网络的训练后,获得了校正后网络的权值和阀值.仿真实验结果表明,采取从输出信号中提取融合信息的方式,利用神经网络的信息融合功能,能逼近一个校正平面,从而较好地解决了传感器误差综合校正问题.     

基于ANN逆模型补偿的复合控制系统仿真

为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器＋前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。     

基于人工神经网络的植被覆盖遥感反演方法研究

使用新型遥感数据-“北京一号”小卫星数据,采用BP神经网络法对密云水库流域内的植被覆盖进行反演,并将结果与传统回归分析法和NDVI像元二分法进行比较。结果表明：在山区植被信息遥感反演算法中,神经网络方法以其对非线性过程的精确模拟而具有比传统算法更高的精度,尤其对于遥感反演算法难度较大的山区植被覆盖信息提取效果较好。 