基于神经网络的地形匹配方法研究

提出了基于神经网络实现多特征融合的地形匹配算法，充分利用地形的各种不同的统计特征和几何特征，构造了一种地形匹配网络模型．通过对实时图和基准图的分析，给出了计算网络节点之间的权值函数，建立了网络系统能量方程，通过求系统的最小能量得到最佳匹配位置．由于网络能融合地形的不同统计特征和几何特征，所以算法大大提高了系统的抗干扰能力和定位精度，适合于实时图容易发生畸变的地形匹配领域．实验结果表明，定位精度和抗干扰能力均优于传统的地形匹配方法．     

Formulation optimization technique based on artificial neural network in salbutamol sulfate osmotic pump tablets

The aim of this study was to develop a formulation optimization technique in which an artificial neural network (ANN) was incorporated; 30 kinds of salbutamol sulfate osmotic pump tablets were prepared, and their dissolution tests were performed. The amounts of hydroxypropyl methylcellulose (HPMC), polyethylene glycol 1500 (PEG1500) in the coating solution, and the coat weight were selected as the causal factors. Both the average drug release rate v for the first 8 hr and the correlation coefficient r of the accumulative amount of drug released andtime were obtained as release parameters to characterize the release profiles. A set of release parameters and causal factors was used as training data for the ANN, and another set of data was used as test data. Both sets of data were fed into a computer to train the ANN. The training process of theANN was completed until a satisfactory value of error function E for the test data was obtained. The optimal formulation produced by the technique gave the satisfactory release profile since the observed results coincided well with the predicted results. These findings demonstrate that an ANN is quite useful in the optimization of pharmaceutical formulations.     

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     

基于BP人工神经网络的混凝土强度预测

混凝土强度是结构设计中控制的主要指标,其数值决定于水灰比、胶凝材料用量、矿物掺量、外加剂用量等多种因素,常规计算混凝土强度的公式因个人理解的不同而各异,一种仿生模型—人工神经网络则能很好地解决这个难题,文中尝试用人工神经网络对不同混凝土强度进行预测,结果表明此模型的可靠度很高,可以用以优化混凝土的试配,节约大量的时间、人力、物力和财力.     

Artificial neural networks for pattern recognition

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed. This paper is mostly a consolidation of work reported by several researchers in the literature, some of which is cited in the references. The author has borrowed several ideas and illustrations from the references quoted in this paper.     

Prediction of hopper discharge rate using combined discrete element method and artificial neural network

An Artificial Neural Network (ANN) was developed to predict the mass discharge rate from conical hoppers. By employing Discrete Element Method (DEM), numerically simulated flow rate data from different internal angles (20°–80°) hoppers were used to train the model. Multi-component particle systems (binary and ternary) were simulated and mass discharge rate was estimated by varying different parameters such as hopper internal angle, bulk density, mean diameter, coefficient of friction (particle-particle and particle-wall) and coefficient of restitution (particle-particle and particle-wall). The training of ANN was accomplished by feed forward back propagation algorithm. For validation of ANN model, the authors carried out 22 experimental tests on different mixtures (having different mean diameter) of spherical glass beads from different angle conical hoppers (60° and 80°). It was found that mass discharge rate predicted by the developed neural network model is in a good agreement with the experimental discharge rate. Percentage error predicted by ANN model was less than ±13%. Furthermore, the developed ANN model was also compared with existing correlations and showed a good agreement.     

航天光学遥感器信噪比的人工神经网络评价

提出基于人工神经网络进行航天光学遥感器信噪比评价的方法,首先对航天遥感图像进行分析,从图像中将与景物结构和噪声有关的特征向量分别提取出来,作为ANN的输入。网络通过对大量信噪比已知的图像样本训练后,可完成对航天光学遥感器传输下来的任意一幅地面景物图像进行系统的信噪比测试,从而避免了采用特定景物目标进行测量中的诸多弊端,测量平均误差低于10%。     

Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log k_p) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r² = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r² = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.     

A mechanism motion error sensitivity analysis method based on principal component analysis and artificial neural network

Present sensitivity analysis of motion error usually focuses on the trajectory deviation of the mechanism, which inevitably introduces an intractable time dependent problem. For efficiently and accurately measuring the motion error of the planar mechanism with dimension and clearance uncertainties by global sensitivity analysis (GSA), a novel method is proposed in this work. By applying the principal component analysis (PCA), the motion error is transformed into new vector output and cleverly avoids the time dependent problem. To ensure the accuracy of PCA in the case of small samples, the Bootstrap method is introduced. Based on the PCA results, the artificial neural network (ANN) surrogate model is established between the input variables and the vector output. Then the classical variance-based GSA method is applied to obtain the variable importance ranking for different PCs, and the synthesized GSA indices are introduced. Four representative examples are studied to demonstrate the versatility and effectiveness of the proposed method.     

In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network

Prediction/detection of exit burrs is critical in manufacturing automation. In this research, an intelligent burr sensing/monitoring scheme is proposed. Acoustic emission (AE) was selected to detect burr formation during drilling. For effective extraction of information contained in the collected AE signals, wavelet transform (WT) was adopted and the selected features through WT were fed into a back-propagation artificial neural net (ANN) as input vectors. To validate the in-process AE monitoring system, both WT-based ANN and cutting condition-based ANN outputs (cutting speed, feed, drill diameter, etc.) were compared with experimental data. The results show that the proposed scheme is not only efficient with fewer inputs, but more reliable in predicting drilling burr types over cutting condition-based ANN.     

Hybrid artificial neural network based on BP-PLSR and its application in development of soft sensors

A novel hybrid artificial neural network (HANN) integrating error back propagation algorithm (BP) with partial least square regression (PLSR) was proposed to overcome two main flaws of artificial neural network (ANN), i.e. tendency to overfitting and difficulty to determine the optimal number of the hidden nodes. Firstly, single-hidden-layer network consisting of an input layer, a single hidden layer and an output layer is selected by HANN. The number of the hidden-layer neurons is determined according to the number of the modeling samples and the number of the neural network parameters. Secondly, BP is employed to train ANN, and then the hidden layer is applied to carry out the nonlinear transformation for independent variables. Thirdly, the inverse function of the output-layer node activation function is applied to calculate the expectation of the output-layer node input, and PLSR is employed to identify PLS components from the nonlinear transformed variables, remove the correlation among the nonlinear transformed variables and obtain the optimal relationship model of the nonlinear transformed variables with the expectation of the output-layer node input. Thus, the HANN model is developed. Further, HANN was employed to develop naphtha dry point soft sensor and the most important intermediate product concentration (i.e. 4-carboxybenzaldehyde concentration) soft sensor in p-xylene (PX) oxidation reaction due to the fact that there exist many factors having nonlinear effect on them and significant correlation among their factors. The results of two HANN applications show that HANN overcomes overfitting and has the robust character. And, the predicted squared relative errors of two optimal HANN models are all lower than those of two optimal ANN models and the mean predicted squared relative errors of HANN are lower than those of ANN in two applications.     

Prediction of transverse and angular distortions of gas tungsten arc bead-on-plate welding using artificial neural network

The angular distortion and transverse shrinkage are often generated in gas tungsten arc (GTA) bead-on-plate welding process, which leads to additional costs of rework. Therefore, it is beneficial to estimate the welding deformations prior to bead-on-plate welding in terms of several process parameters. This paper presents the development of a back propagation neural (BPN) network model for the prediction of angular distortion and transverse shrinkage generated in GTA bead-on-plate welding process. The model is based on the results from finite element (FE) simulations. The GTA bead-on-plate welding for S304L stainless steel was simulated using finite element method, and experiments were conducted to verify the accuracy of the FE model. The experimental results were also used as testing samples for the BPN model. Welding speed, current and voltage were considered as the input parameters and the angular distortion and transverse shrinkage were the output parameters in the development of the BPN model. The correlation coefficients and percentage errors for all the samples were calculated to evaluate the prediction accuracy of BPN model. The results show that the BPN model developed in this study can predict the angular distortion and transverse shrinkage with reasonable accuracy.     

A hybrid artificial neural network method with uniform design for structural optimization

This paper presents a new hybrid artificial neural network (ANN) method for structural optimization. The method involves the selection of training datasets for establishing an ANN model by uniform design method, approximation of the objective or constraint functions by the trained ANN model and yields solutions of structural optimization problems using the sequential quadratic programming method (SQP). In the proposed method, the use of the uniform design method can improve the quality of the selected training datasets, leading to a better performance of the ANN model. As a result, the ANN dramatically reduces the number of required trained datasets, and shows a good ability to approximate the objective or constraint functions and then provides an accurate estimation of the optimum solution. It is shown through three numerical examples that the proposed method provides accurate and computationally efficient estimates of the solutions of structural optimization problems.     

Reliability analysis of structures using neural network method

In order to predict the failure probability of a complicated structure, the structural responses usually need to be estimated by a numerical procedure, such as finite element method. To reduce the computational effort required for reliability analysis, response surface method could be used. However the conventional response surface method is still time consuming especially when the number of random variables is large. In this paper, an artificial neural network (ANN)-based response surface method is proposed. In this method, the relation between the random variables (input) and structural responses is established using ANN models. ANN model is then connected to a reliability method, such as first order and second moment (FORM), or Monte Carlo simulation method (MCS), to predict the failure probability. The proposed method is applied to four examples to validate its accuracy and efficiency. The obtained results show that the ANN-based response surface method is more efficient and accurate than the conventional response surface method.     

Hierarchical supply chain planning using artificial neural networks to anticipate base-level outcomes

Most Advanced Planning Systems decompose the task of production planning according to the planning horizon in two levels, mid-term and short-term planning. The mid-term planning level sets the targets for the short-term level. In response, the short-term planning level gives feedback to the mid-term level. Moreover, due to detailed knowledge, the short-term planning level should provide relevant input to the mid-term planning run. To compute accurate targets for the short-term planning level the mid-term planning should anticipate its major behaviour. In this article we present an artificial neural network based anticipation of a short-term planning level for a single-stage, multi-product flow line production environment.     

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。     

An artificial neural network for modeling reliability, availability and maintainability of a repairable system

The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system.     

Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m³ day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m³ day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.     

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。