An artificial intelligence model to estimate the fat addition ratio for the mixing process in the animal feed industry

In animal feed pellets, the fat content is obtained either from the feed ingredients or is directly added during processing. Additional fat is required when the fat level in the feed ingredients is less than the desired level. This fat can be added either during the mixing process or after the pelleting process. However, adding fat at different time leads to different results. The addition of an increasing amount of fat during the mixing process decreases the pellet durability but enhances the pellet production rate. To avoid a reduction in the pellet durability, limiting the inclusion of fats in the mixer is suggested. The use of suitable fat addition ratios during mixing and after pelleting can improve the pellet quality and the production capability. Many factors significantly affect the decision of how much fat to add, such as the fiber inclusion content in the feed formulation, pellet die size, required feed durability, total required fat, and required additional fat. Due to frequent changes in the feed mix, anticipating the suitable amount of fat addition during the mixing process becomes a cumbersome task for a mill. In this paper, a model for estimating the amount of fat required in the mixer for each feed formulation is proposed. The model is based on the local linear map (LLM) and the back-propagation neural network (BPNN) methods. The LLM is used to identify which feed formulations require the addition of fat both during mixing and after pelleting, whereas the BPNN is employed for estimating the proper total fat required in the mixer, and the ratio of fat to add during the mixing process is subsequently estimated by subtracting the fat in the raw material from the total fat required in the mixer. The model is developed using data from one the largest feed mills in Thailand. The proposed model provides an accurate prediction and is practical for implementation in the mill that was studied.     

Quality detection and classification for ultrasonic welding of carbon fiber composites using time-series data and neural network methods

Ultrasonic welding is a novel and efficient technique for joining carbon fiber composites in the automotive industry. Weld quality detection and classification is important to its adoption and deep neural network models are a promising method for this purpose. However, it is difficult to collect the large volume of data needed to train these models with laboratory experiments due to the cost of the materials and cost of weld experiments. Using a limited set of experimental data, a copula multivariate Monte Carlo simulation is proposed to generate large data sets of time-series process signals with similar statistical distributions as the experimental data. The experimental data and simulated data are used to train Bayesian regularized neural network (BRNN) and convolutional neural network (CNN) models to predict weld quality classifications in ultrasonic welding. The results show that BRNN and CNN have similar classification accuracy. But CNN has an advantage in training efficiency compared with BRNN. Both neural-network-based methods were found to be more accurate than support vector machine and k-nearest neighbor methods, when applied to both features extracted from signals and full time-series-based process signals.     

Pattern storage and processing in attractor networks with short-time synaptic dynamics

Neurophysiological experiments show that the strength of synaptic connections can undergo substantial changes on a short time scale. These changes depend on the history of the presynaptic input. Using mean-field techniques, we study how short-time dynamics of synaptic connections influence the performance of attractor neural networks in terms of their memory capacity and capability to process external signals. For binary discrete-time as well as for firing rate continuous-time neural networks, the fixed points of the network dynamics are shown to be unaffected by synaptic dynamics. However, the stability of patterns changes considerably. Synaptic depression turns out to reduce the storage capacity. On the other hand, synaptic depression is shown to be advantageous for processing of pattern sequences. The analytical results on stability, size of the basins of attraction and on the switching between patterns are complemented by numerical simulations.     

深度学习的研究进展与发展

深度学习是基于数据表示的一类更广的机器学习方法,它的出现不仅推动了机器学习的发展,而且促进了人工智能的革新。对深度学习的几种典型模型进行研究与对比。首先介绍受限玻尔兹曼机、深度置信网络、自编码器等无监督学习模型,对其结构、原理和优缺点进行了详细探讨。讨论卷积神经网络、循环神经网络和深度堆叠网络等监督学习模型,分别从模型架构和工作原理来评价与分析。对深度学习的典型模型进行对比分析,将深度置信网络和卷积神经网络应用在手写体数字识别任务中,结果证实深度学习比传统的神经网络具有更好的识别性能。最后探讨深度学习未来的发展与挑战。     

Prediction and optimization of depth of penetration for stainless steel gas tungsten arc welded plates using artificial neural networks and simulated annealing algorithm

The quality of a weld joint is highly influenced by depth of penetration. Hence, accurate prediction and maximization of depth of penetration is highly essential to ensure a good-quality joint. This paper highlights the development of neural network model for predicting depth of penetration and optimizing the process parameters for maximizing depth of penetration using simulated annealing algorithm. The process parameters chosen for the study are welding current, welding speed, gas flow rate and welding gun angle. The chosen output parameter was depth of penetration. The experiments were conducted based on design of experiments using fractional factorial with 125 runs. Using the experimental data, feed-forward backpropagation neural network model was developed and trained using Levenberg–Marquardt algorithm. It was found that ANN model based on network 4-15-1 predicted depth of penetration more accurately. A mathematical model was also developed correlating the process parameters with depth of penetration for doing optimization. A source code was developed in MATLAB to do the optimization. The optimized process parameters gave a value of 3.778 mm for depth of penetration.     

基于BP人工神经网络的软件质量评估

针对软件质量评估的课题,提出了一种基于BP人工神经网络的软件质量评估方法,提高软件质量评估的准确性.首先,论文介绍了人工神经网络的基本原理和软件质量评估的基本过程.然后选取适当软件质量特征构建基于BP人工神经网络的评估体系,分别进行BP网络学习和验证数据测试的实验.通过测试得到的数据结果,证明该方法能够准确地评估软件质量.     

基于RBF神经网络的赤潮预测方法

赤潮是一种由多因素综合作用引发的生态异常现象,具有突发性及非线性等特点。对其进行预测预报一直是海洋科学研究的热点。简要介绍了RBF神经网络的基本原理,探讨了应用该人工神经网络进行赤潮预测的方法。利用RBF神经网络模型对赤潮灾害监测数据进行仿真实验,并对结果进行了分析。     

Financial time series prediction by a random data-time effective RBF neural network

An improved neural network of time series predicting is presented in this paper. We introduce a random data-time effective radial basis function neural network in determination of the output weights, the center vectors and the widths in the hidden layer of the network. In the training modeling, we consider that the historical data on the financial market is key to the investors’ decision-making for their investing positions, and the impact of historical data depends closely on the time. We develop a random data-time effective function to describe this impact strength, and a weight is given to each of the historical data, where a drift function and a random Brownian volatility function are applied to express the behavior of the time strength. Further, this neural network is applied to the prediction of financial price series of crude oil, SSE, N225 and DAX. The empirical experiments show that the proposed neural network results in better performance in financial time series forecasting and is advantageous in increasing the forecasting precision.     

基于改进的卷积神经网络的道路井盖缺陷检测研究

道路井盖缺陷检测对于道路维护与安全至关重要,论文提出了一种改进的卷积神经网络算法,可实现井盖缺陷的快速、准确检测。算法对卷积神经网络的激活函数模型进行了改进,针对Relu激活函数在输入小于零时输出设为零,导致部分缺陷信息丢失问题,设计了MReLu和BReLu两种改进激活函数。在此基础上,为了增强神经网络模型的特征表达能力,提出了双层激活函数模型。最后,在公共数据集MNIST,CIFAR-10上进行了比较实验,网络主要参数有批处理大小(batch size)为32,最大迭代次数为1000次,学习率为0.0001,每经过5000次迭代衰减50%。实验结果表明,基于改进后的激活函数和应用双层激活函数所构造的卷积神经网络,大大减少了训练参数,不仅收敛速度更快,而且可以更加有效地提高分类的准确率。     

SAR图像目标识别的卷积神经网模型

目的 合成孔径雷达图像目标识别可以有效提高合成孔径雷达数据的利用效率。针对合成孔径雷达图像目标识别滤波处理耗时长、识别精度不高的问题,本文提出一种卷积神经网络模型应用于合成孔径雷达图像目标识别。方法 首先,针对合成孔径雷达图像特点设计特征提取部分的网络结构;其次,代价函数中引入L2范数提高模型的抗噪性能和泛化性;再次,全连接层使用Dropout减小网络的运算量并提高泛化性;最后研究了滤波对于网络模型的收敛速度和准确率的影响。结果 实验使用美国运动和静止目标获取与识别数据库,10类目标识别的实验结果表明改进后的卷积神经网络整体识别率（包含变体）由93.76%提升至98.10%。通过设置4组对比实验说明网络结构的改进和优化的有效性。卷积神经网络噪声抑制实验验证了卷积神经网络的特征提取过程对于SAR图像相干斑噪声有抑制作用,可以省去耗时的滤波处理。结论 本文提出的卷积神经网络模型提高了网络的准确率、泛化性,无需耗时的滤波处理,是一种合成孔径雷达图像目标识别的有效方法。     

应用神经网络测量凝析天然气流量

为了精确测量凝析天然气的气液相流量,研究了将神经网络用于气液两相流流量测量的方法。以中国石油大学自动化系试制的流量检测仪表样机为硬件基础,分析了仪表的差压波动信号,并通过比较F比值确定了特征提取方法。根据多相流存在多种流型的特点,采用了首先识别流体流型再计算其流量的方法。研究表明:神经网络在凝析天然气流量测量中具有较大的应用潜力。但要获得较高的层量精度,还需要更为准确的信号特征提取方法以及更加合理的网络结构。     

两层级神经网络及在中医智能诊断中的应用 *

通过分析中医临床数据的特性 ,将临床数据分为低层级数据和高层级数据 ,每个层级数据又分为全局输入参数和局部输入参数。基于这些概念 ,建立了一种两层级神经网络 ,低层级子神经网络局部处理低层级数据,高层级子神经网络综合处理高层级数据和低层级子神经网络的输出结果。这样的结构不仅能有效地刻画中医辨证问题 ,而且简化了计算 ,提高了学习收敛速度。实验结果表明 ,这种两级神经网络可以较好地应用于具有复杂数据关系的中医辨证智能计算。     

基于时间点过程的时间序列预测模型

对城市中发生的事件进行有效预测,可以为政府避免、控制或减轻相关的社会风险提供决策支撑。首先,提出基于积分求导法的条件强度函数式,提高序列预测精度;其次,构建基于递归神经网络和累积危险函数的时间点过程模型,通过递归神经网络捕获历史事件的非线性依赖关系,利用全连接网络获得累积危险函数;最后,选择具有代表性的合成数据集和真实数据集对几种模型的性能进行对比分析。实验结果表明,所提模型可以更好地进行城市事件的时间序列预测,在平均绝对误差、平均负对数似然值等方面均优于传统的时间点过程模型,说明了模型的优越性。     

Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring

With the growing of automation in manufacturing, process quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for quality control in the case of serially correlated data. In particular, the Elman’s recurrent neural network is proposed for manufacturing process quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for quality monitoring in the case of autocorrelated process data.     

联合结构相似性与类信息的图像分类

针对卷积神经网络训练收敛速度慢的问题，提出了一种加权的联合结构相似性和类信息监督训练的方法。首先，针对小图像，设计一个能有效提取图像高级别信息的卷积神经网络。其次，建立加权的联合结构相似性和类信息损失函数训练卷积神经网络。最后，通过mnist手写数字和cifar10图像分类实验验证所设计网络的有效性。实验结果表明，所设计的网络在mnist手写数字和cifar10数据集上的图像分类错误率分别为0.33%和11%。在未进行扩增mnist数据集的前提下，所设计的网络的性能超过了该数据集上所有单网络的性能；在cifar10数据集上，所设计的网络能以较少的计算量获得较高的图像分类准确率。同时，联合结构相似性和类信息损失的监督训练能加快网络的训练速度。     

A knowledge-based system for the prediction of surface roughness in turning process

In the present work, a knowledge-based system is developed for the prediction of surface roughness in turning process. Neural networks and fuzzy set theory are used for this purpose. Knowledge acquired from the shop floor is used to train the neural network. The trained network provides a number of data sets, which are fed to a fuzzy-set-based rule generation module. A large number of IF–THEN rules are generated, which can be reduced to a smaller set of rules by using Boolean operations. The developed rule base may be used for predicting surface roughness for given process variables as well as for the prediction of process variables for a given surface roughness. The concise set of rules helps the user in understanding the behavior of the cutting process and to assess the effectiveness of the model. The performance of the developed knowledge-based system is studied with the experimental data of dry and wet turning of mild steel with HSS and carbide tools.     

多层前向人工神经网络图像分类算法

传统人工神经网络的输入均为向量形式,而图像由矩阵形式表示,因此,在用人工神经网络进行图像处理时,图像将以向量形式输入至神经网络,这破坏了图像的结构信息,从而影响了图像处理的效果。为了提高网络对图像的处理能力,文中借鉴了深度学习的思想与方法,引进了具有矩阵输入的多层前向神经网络。同时,采用传统的反向传播训练算法(BP)训练该网络,给出了训练过程与训练算法,并在USPS手写数字数据集上进行了数值实验。实验结果表明,相对于单隐层矩阵输入前向神经网络(2D-BP),所提多层网络具有较好的分类效果。此外,对于彩色图片分类问题,利用所提出的2D-BP网络,给出了一个有效的可行方法。     

超图学习综述:算法分类与应用分析

随着图结构化数据挖掘的兴起,超图作为一种特殊的图结构化数据,在社交网络分析、图像处理、生物反应解析等领域受到广泛关注.研究者通过解析超图中的拓扑结构与节点属性等信息,能够有效解决实际应用场景中所遇到的如兴趣推荐、社群划分等问题.根据超图学习算法的设计特点,将其划分为谱分析方法和神经网络方法,根据方法对超图处理的不同手段...     

数据驱动的RF信号深度调制识别方法

基于卷积结构的信号调制识别神经网络的识别性能受信号调制类型种类限制。例如,在12 dB信噪比条件下,同时对24种信号调制类型进行识别,其识别准确率仅为80%。若需要进一步提高识别性能,则要求更复杂的网络模型,导致网络训练所需数据集规模和硬件资源成本增大。鉴于此,针对无线电信号特征,设计一种适用于无线电信号调制识别的紧致残差神经网络,将其作为信号调制类型特征学习和特征提取工具,实现从原始I、Q数据到信号调制类型的端到端识别。利用迁移学习降低网络重新训练所需样本数,增强在无线信道响应发生变化时的环境适应能力,降低训练阶段所需的硬件资源和训练数据集规模。研究表明,当信道脉冲响应改变时,所提的信号调制识别神经网络在信噪比为12 dB条件下的识别性能达到95%,多个对比实验验证本文所设计神经网络的识别性能具有优势。     

基于Temporal rule的忆阻神经网络电路

忆阻器是一种动态特性的电阻,其阻值可以根据外场的变化而变化,并且在外场撤掉后能够保持原来的阻值,具有类似于生物神经突触连接强度的特性,可以用来存储突触权值。在此基础上,为了实现基于Temporal rule对IRIS数据集识别学习的功能,建立了以桥式忆阻器为突触的神经网络SPICE仿真电路。采用单个脉冲的编码方式,脉冲的时刻代表着数据信息,该神经网络电路由48个脉冲输入端口、144个突触、3个输出端口组成。基于Temporal rule学习规则对突触的权值修改,通过仿真该神经网络电路对IRIS数据集的分类正确率最高能达到93.33%,表明了此神经系统结构设计在类脑脉冲神经网络中的可用性。