基于决策树的注采连通关系判别研究

判别注采连通关系的传统方法是由人工通过对比测井曲线, 逐个层位地进行连通关系的识别和分类, 这种方式工作量大、耗费劳动力多、判别效率较低。针对大庆油田注采连通关系判别的这种现状, 从油藏开发数据中提取并构建出注采井间的相对特征, 使用CART（分类回归树）算法建立了注采连通关系自动判别的决策树模型。实验结果表明, 该方法具有操作简单、判别速度快等优点, 在提高连通关系判别效率的同时保证了有较高的精度。     

基于OWO-HWO算法的MLP网络在织物染色配色中的应用

讨论了具有非线性、不确定特性的织物染色配色过程建模与仿真问题。针对传统的织物染色配色方法效果差、精确度不高和难以达到期望结果的问题,结合MLP神经网络的特点,提出了基于OWO-HWO算法训练的MLP神经网络,同时分别优化网络输入层到隐层和隐层到输出层的权值,并利用基于OWO-HWO算法的MLP神经网络建立织物染色配色模型。针对此种模型,利用NuMap神经网络软件进行仿真实验。仿真结果表明,该配色模型收敛速度快,精确度高,在解决织物染色配色问题上取得了令人满意的配色效果。     

油田产量多变量预测模型的优化

油田开发是一个复杂的多变量非线性动力学系统，为有效地预测油田产量，确保油田生产过程高产稳产，该文提出采用多元线性回归与神经网络相结合的方法对油田产量多变量预测模型进行优化。首先基于回归分析的“后退法”对影响产量的变量进行优选，然后通过神经网络对优选后的变量进行训练得到最终的预测模型，从而实现神经网络与多元线性回归相结合建立多变量预测模型。实际应用结果表明，优化后的模型简洁实用，可以在一定程度上提高模型的预测精度，并减少建模预测所需数据量。     

贝叶斯正规化算法在油藏参数拟合方面的应用

通过已知测井资料对油藏储量进行预测,是目前石油行业一个重要的研究课题。文章介绍了一种基于贝叶斯正规化算法的BP神经网络,并把网络应用到油藏参数拟合过程中的具体方法,该方法对提高石油生产效率、降低成本具有很大的作用。     

基于复杂模拟的战斗机系统失效敏感性分析

战斗机的系统试验往往成本较高，随着战斗机系统的日益复杂化，基于计算机模拟的表征系统失效信息的功能响应量计算也越来越耗时费力。针对此类基于复杂计算机模拟的敏感性分析问题，提出了一种结合Sobol方法和基于主动学习的Kriging模型的敏感性分析方法，称之为AK-S（Adaptive-Kriging-based Sobol）方法，AK-S方法通过Kriging预测来代替真实响应值计算，因而可以更加高效地计算各输入变量的敏感性指标并得到重要度排序。通过与直接蒙特卡洛法（直接法，MC）和传统Sobol法对数值算例的处理结果进行对比，AK-S方法的计算效率和精度得到了证明。最后，AK-S方法被应用于基于复杂模拟的实际工程案例的失效敏感性分析，并获得了敏感性指标。AK-S算法被证明在同等计算精度的条件下，其效率大大高于MC和传统Sobol法，能很好地解决工程中基于复杂计算机模拟下的失效敏感性分析问题。     

基于Bitmap的油水井采注优化实时推理引擎

针对油田油水井采注优化业务中,油水井数据量大、地层结构复杂以及人类经验多的特点,分析了传统推理方法在油田采注实时优化处理过程中的不足,采用事件处理思想,提出了一种基于Bitmap事件编码与匹配机制的推理引擎,有效地实现了对无效事件的过滤并提升了事件与规则的匹配效率.在油田实际数据试验平台上对该方法进行了验证并与RETE算法、LFA（Linear forward-chaining）算法的性能对比,结果验证了本文方法在实时推理能力上的有效性.     

基于MLP神经网络的分组密码算法能量分析研究

随着社会对信息安全需求的不断提高,各行各业愈加重视对信息技术的应用,而密码算法作为其中的基础内容,学者们对其进行了大量的研究。基于此,文章在阐述了密码算法能量分析和MLP神经网络内涵的基础上,分析了基于MLP神经网络的能量分析流程,并通过重构密码算法设计提出了基于MLP神经网络的分组密码算法,实验表明该模型具有较强的预测性,具有训练次数少、时间短的优点,且迭代次数增加时,模型的预测精度显著提升。     

基于贝叶斯网络分类器的产品故障率分类研究*

针对装备在不同配置及使用环境的条件下运行的故障率等级差异,详细介绍并分析了现有各贝叶斯分类器的特点和构建算法。在此基础上,提出了基于贝叶斯网络的产品故障分类模型建模方法用于指导实际分类任务的模型建立和应用。通过法国某装备生产企业的实例分析,实验结果证明在所有的贝叶斯网络分类器及传统的决策树C4.5分类器中,树型朴素贝叶斯分类器能够取得最好的分类效果,并为后续的维修资源配置及产品运行能力优化提供有效的理论支持。     

融合MLP与循环神经网络算法的语法自动纠错方法研究

为了进一步提升语法自动纠错技术的实用性，研究对以循环神经网络为核心的Sep2Sep模型进行优化改进，引入双向LSTM循环神经网络，将基于双向LSTM的Sep2Sep模型与MLP神经网络相结合构建语法自动纠错系统，并通过测试实验验证语法自动纠错系统的准确率。研究结果表明，研究所设计的语法自动纠错系统F_0.5值为56.37,P值和R值分别为66.78和35.09,检测准确率较高。纠错系统的运行响应时间保持在1.34 s,能在多个检测目标并发情况下进行快速系统响应。研究利用双向LSTM和MLP神经网络解决传统纠错模型的梯度爆炸问题，并采用分布式架构提升自动纠错系统的运行能力，对进一步加强自动语法纠错技术的实用性具有重要意义。     

储气库注采分析优化设计软件GasPAD-Stor的研制

储气库建设是天然气管道生产的重要调峰手段。为了更有效地设计和利用地下储气库,本文研究了储气库的建库机理、注采参数、运行控制。根据储气库管柱结构特点,建立了三维管柱受力模型,分析计算了管柱受力、冲蚀、携液、环空氮气柱压力等重要参数,研发了一套储气库注采分析优化设计软件GasPAD-Stor。该软件具备储气库库容设计、运行优化、调峰设计、注采能力分析、注采管柱设计、地面工艺设计、经济评价等功能。软件采用C/S架构,用户可以根据实际需要配置软件模块。该软件为开展储气库注采动态模拟、运行、调峰等提供了研究手段。     

Achieving a More Robust Neural Network Model for Control of a MR Damper by Signal Sensitivity Analysis

Most neural network models can work accurately on their trained samples, but when encountering noise, there could be significant errors if the trained neural network is not robust enough to resist the noise. Sensitivity to perturbation in the control signal due to noise is very important for the prediction of an output signal. The goal of this paper is to provide a methodology of signal sensitivity analysis in order to enable the selection of an ideal Multi-Layer Perception (MLP) neural network model from a group of MLP models with different parameters, i.e. to get a highly accurate and robust model for control problems. This paper proposes a signal sensitivity which depends upon the variance of the output error due to noise in the input signals of a single output MLP with differentiable activation functions. On the assumption that noise arises from additive/multiplicative perturbations, the signal sensitivity of the MLP model can be easily calculated, and a method of lowering the sensitivity of the MLP model is proposed. A control system of a magnetorheological (MR) fluid damper, which is a relatively new type of device that shows the future promise for the control of vibration, is modelled by MLP. A large number of simulations on the MR damper’s MLP model show that a much better model is selected using the proposed method.     

Commitment and typicality measures for the Self-Organizing Map

Soft classification using Kohonen's Self-Organizing Map (SOM) has not been explored as thoroughly as the Multi-Layer-Perceptron (MLP) neural network. In this paper, we propose two non-parametric algorithms for the SOM to provide soft classification outputs. These algorithms, which are labelling-frequency-based, are called SOM Commitment (SOM-C) and SOM Typicality (SOM-T), expressing in the first case the degree of commitment the classifier has for each class for a specific pixel and in the second case, how typical that pixel's reflectances are of those upon which the classifier was trained. To evaluate the two proposed algorithms, soft classifications of a Satellite Pour l'Observation de la Terre (SPOT) High Resolution Visible (HRV) image and an Airborne Visible Infrared Imaging Spectrometer (AVIRIS) image were undertaken. Both traditional soft classifiers, i.e. Bayesian posterior probability and Mahalanobis typicality classifier, and the most frequently used non-parametric neural network model, i.e. MLP, were used as a comparison. Principal-components analysis (PCA) was used to explore the relationship between these measures. Results indicate that great similarities exist between the SOM-C, MLP and the Bayesian posterior probability classifiers, while the SOM-T corresponds closely with Mahalanobis typicality probabilities. However, as implemented, they have the advantage of being non-parametric. The proposed measures significantly outperformed Bayesian and Mahalanobis classifiers when using the hyperspectral AVIRIS image.     

Modeling plasma surface modification of textile fabrics using artificial neural networks

In this paper, a neural network approach is used to understand the effects of fabric features and plasma processing parameters on fabric surface wetting properties. In this approach, fourteen features characterizing woven structures and two plasma parameters are taken as input variables, and the water contact angle cosine and the capillarity height of woven fabrics as output variables. In order to reduce the complexity of the model and effectively learn the network structure from a small number of data, a fuzzy logic based method is used for selecting the most relevant parameters which are taken as input variables of the reduced neural network models. With these relevant parameters, we can effectively control the plasma treatment by selecting the most appropriate fabric materials. Two techniques are used for improving the generalization capability of neural networks: (i) early stopping and (ii) Bayesian regularization. A methodology for optimizing such models is described. The learning abilities and prediction capabilities of the neural net models are compared in terms of different statistical performance criteria. Moreover, a connection weight method is used to determine the relative importance of each input variable in the networks. The obtained results show that neural network models could predict the process performance with reasonable accuracy. However, the neural model trained using Bayesian regularization provides the best results. Thus, it can be concluded that Bayesian network promises to be a valuable quantitative tool to evaluate, understand, and predict woven fabric surface modification by atmospheric air-plasma treatment.     

An on-line wastewater quality predication system based on a time-delay neural network

The biological treatment process in a wastewater treatment system is a very complex process. The efficiency of the treatment is usually measured by laboratory tests, which typically take five days. In this paper, a time-delay neural network (TDNN) modeling method is proposed for predicting the treatment results. As the first step, a sensitivity analysis performed on a multi-layer perceptron (MLP) network model is used to reduce the input dimensions of the model. Then a TDNN model is further used to improve the performance of the original MLP network model. Subsequently, an on-line prediction and model-updating strategy is proposed and implemented. Simulations using industrial process data show that the prediction accuracy can be improved by the on-line model updating.     

Sensitivity analysis of multilayer perceptron with differentiableactivation functions

In a neural network, many different sets of connection weights can approximately realize an input-output mapping. The sensitivity of the neural network varies depending on the set of weights. For the selection of weights with lower sensitivity or for estimating output perturbations in the implementation, it is important to measure the sensitivity for the weights. A sensitivity depending on the weight set in a single-output multilayer perceptron (MLP) with differentiable activation functions is proposed. Formulas are derived to compute the sensitivity arising from additive/multiplicative weight perturbations or input perturbations for a specific input pattern. The concept of sensitivity is extended so that it can be applied to any input patterns. A few sensitivity measures for the multiple output MLP are suggested. For the verification of the validity of the proposed sensitivities, computer simulations have been performed, resulting in good agreement between theoretical and simulation outcomes for small weight perturbations.     

A quantified sensitivity measure for multilayer perceptron to input perturbation

The sensitivity of a neural network's output to its input perturbation is an important issue with both theoretical and practical values. In this article, we propose an approach to quantify the sensitivity of the most popular and general feedforward network: multilayer perceptron (MLP). The sensitivity measure is defined as the mathematical expectation of output deviation due to expected input deviation with respect to overall input patterns in a continuous interval. Based on the structural characteristics of the MLP, a bottom-up approach is adopted. A single neuron is considered first, and algorithms with approximately derived analytical expressions that are functions of expected input deviation are given for the computation of its sensitivity. Then another algorithm is given to compute the sensitivity of the entire MLP network. Computer simulations are used to verify the derived theoretical formulas. The agreement between theoretical and experimental results is quite good. The sensitivity measure can be used to evaluate the MLP's performance.     

Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and Multi-layer Perceptron Neural Network

In many engineering projects, the soil compression coefficient is an important parameter used for estimating the settlement of soil layers. The common practice of determining the soil compression coefficient via the oedometer test is time-consuming and expensive. This study proposes a machine learning solution to replace the conventional tests used for obtaining the coefficient of soil compression. The new approach is an integration of the Multi-Layer Perceptron Neural Network (MLP Neural Nets) and Particle Swarm Optimization (PSO). These two computational intelligence methods work synergistically to establish a prediction model of soil compression coefficient. The PSO metaheuristic is employed to optimize the MLP Neural Nets model structure. To train and validate the proposed method, named as PSO-MLP Neural Nets, a dataset of 154 soil samples featuring 12 influencing factors has been collected from the geotechnical investigation process of a high-rise building project. Experimental results show that the proposed PSO-MLP Neural Nets has attained the most accurate prediction of the soil compression coefficient performance with RMSE = 0.0267, MAE = 0.0145, and R² = 0.884. The result of the proposed model is significantly better than those obtained from other benchmark methods including the backpropagation neural network, the radial basis function neural network, the support vector regression, the random forest, and the Gaussian process. Based on the experimental results, the newly constructed PSO-MLP Neural Nets is very potential to be a new alternative to assist geotechnical engineers in design phase of civil engineering projects.     

Evaluation of unsupervised feature extraction neural networks for face recognition

In this paper, new appearances based on neural networks (NN) algorithms are presented for face recognition. Face recognition is subdivided into two main stages: feature extraction and classifier. The suggested NN algorithms are the unsupervised Sanger principal component neural network (Sanger PCNN) and the self-organizing feature map (SOFM), which will be applied for features extraction of the frontal view of a face image. It is of interest to compare the unsupervised network with the traditional Eigenfaces technique. This paper presents an experimental comparison of the statistical Eigenfaces method for feature extraction and the unsupervised neural networks in order to evaluate the classification accuracies as comparison criteria. The classifier is done by the multilayer perceptron (MLP) neural network. Overcoming of the problem of the finite number of training samples per person is discussed. Experimental results are implemented on the Olivetti Research Laboratory database that contains variability in expression, pose, and facial details. The results show that the proposed method SOFM/MLP neural network is more efficient and robust than the Sanger PCNN/MLP and the Eigenfaces/MLP, when used a few number of training samples per person. As a result, it would be more applicable to utilize the SOFM/MLP NN in order to accomplish a higher level of accuracy within a recognition system.     

A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features

In this paper, Levenberg–Marquardt feedforward MLP neural network (LMFFNN) was proposed to classify cervical cell images obtained from 100 patients including healthy, low-grade intraepithelial squamous lesion and high-grade intraepithelial squamous lesion cases. This neural network along with extracted cell image features is a new model for cervical cell image classification. The semiautomated cervical cancer diagnosis system is composed of two phases: image preprocessing/processing and feedforward MLP neural network. In the first stage, image preprocessing is done to reduce the existing noises without lowering the resolution. After that, image processing algorithms were applied to manually cropped cell images to achieve a linear plot which includes real components, were used as LMFFNN inputs for classification of cervical cell images. Based on the results, cervical cell images were classified successfully with 100 % correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100 % using LMFFNN method. It was shown there was a good agreement between the expert decision and values gained from the ANN model.