Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

Development of Al_2O_3/TiN Ceramie Cutting Tool Materials by Artificial Neural Networks

The artificial neural networks (ANN) which have broad application were proposed to develop multiphase ceramie cutting tool materials. Based on the back propagation algorithm of the forward multilayer perceptron, the models to predict volume content of composition in particie reinforced ceramies are established. The Al2O3/TiN ceramie cutting tool material was developed by ANN, whose mechanicai properties fully satisfy the cutting requirements.

STUDY ON ARTIFICIAL NEURAL NETWORK FORECASTING METHOD OF WATER CONSUMPTION PER HOUR

An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.

Proton exchange membrane fuel cells modeling based on artificial neural networks

To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

Adaptive prediction system of sintering through point based on self-organize artificial neural network

A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self organize the hidden layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.[

An Improved BP Algorithm and Its Application in Classification of Surface Defects of Steel Plate

Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate.

Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-BP algorithm

Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research. This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-AI alloys. Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-AI alloys, a momentum back-propagation （BP） neural network with a single hidden layer was established. Particle swarm optimization （PSO） was applied to optimize the BP model. In the neural network, the input variables were the contents of Mg, Li and AI, and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-AI alloy＇s composition and its mechanical properties. It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model. The prediction of the influence of AI addition on the mechanical properties of as-cast Mg-Li-AI alloys is consistent with the related research results.

Artificial Neural Networks Model of Evaluating the Schemes of Mine Design

This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the structure of the three\|layer neural network, its learning process, its operating algorithm to realize the evaluation of mine design schemes in a computer and a practical example is also involved in it.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

基于Sine-SSA-BP人工神经网络的腐蚀速率预测研究

海洋工程用钢广泛应用于海洋资源开发;然而,在海洋环境中,由于海洋环境复杂,钢的腐蚀速度大幅加快。为了评估其使用寿命,需要准确地预测钢的腐蚀速率。挂片实验法费时费力,经验模型预测虽然可以快速预测,但因海洋中影响腐蚀的因素较多,准确度较差。本文介绍了一种机器学习方法,即反向传播(BP)神经网络金属腐蚀速率预测模型。本研究创新性地将Sine混沌映射与麻雀搜索优化算法(SSA)引入腐蚀速率预测模型中,并利用2022年采集到的海洋环境要素和腐蚀速率数据导入模型进行训练预测。结果表明,SSA-BP和Sine-SSA-BP神经网络金属腐蚀速率预测模型的误差远低于BP神经网络腐蚀速率预测模型。经过充分的训练和学习,当预测样本数量由5至30逐渐增加时,Sine-SSA-BP预测模型的平均MAPE值为3.5002%,SSA-BP模型的平均MAPE值为6.0900%。

基于麻雀搜索算法优化BP人工神经网络的短期湍流预报模型研究

提出了一种基于麻雀搜索算法优化BP人工神经网络(SSA-BP)的湍流预报模式。首先,采用BP人工神经网络作为湍流预报模型的基础框架。通过对温度、湿度、风速等气象因素的采集和处理,将其作为输入层的特征。然后,利用麻雀搜索算法对BP人工神经网络的权重和偏置进行优化。为了验证该方法的有效性,采用了来自地面气象站的大气湍流数据及气象数据进行实验。实验结果表明,SSA-BP人工神经网络能够成功预测大气湍流的发展趋势,并具有较高的预测精度和稳定性,能够充分利用大气湍流数据中的非线性特征,为湍流预测研究和实际应用提供了有力支持。

Predicting the composition of flux-cored wire claded metal by a neural network

In this paper, an artificial neural network method that can predict the chemical composition of deposited weld metal by CO 2 Shielded Flux Cored Wire Surfacing was studied. It is found that artificial neural network is a good approach on studying welding metallurgy processes that cannot be described by conventional mathematical methods. In the same time we explored a new way to study the no equilibrium welding metallurgy processes.

Application of genetic BP network to discriminating earthquakes and explosions

We developed a GA-BP algorithm by combining the genetic algorithm (GA) with the back propagation (BP) algorithm and established a genetic BP neural network. We also applied the BP neural network based on the BP algorithm and the genetic BP neural network based on the GA-BP algorithm to discriminate earthquakes and explosions. The obtained result shows that the discriminating performance of the genetic BP network is slightly better than that of the BP network.

BFA BASED NEURAL NETWORK FOR IMAGE COMPRESSION

A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the quality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are firstly introduced into neural network in the proposed algorithm. Extensive experiments are conducted on standard testing images and the results show that the pro- posed method can improve the quality of the reconstructed images significantly.

基于ABC-BP神经网络的地铁盾构地表沉降预测

为研究地层参数和盾构掘进参数与地表沉降的非线性关联性,依托南京地铁6号线盾构区间,采用人工蜂群算法ABC优化BP神经网络,建立可预测地表沉降的ABC-BP神经网络模型。连续3个断面地表沉降预测结果表明:ABC-BP神经网络的预测精度和预测稳定性优于BP神经网络,且预测值与实测值一致;ABC-BP神经网络可较为准确地反映盾构机接近监测断面过程中的地表变形演变规律,最终实现地表变形控制的目的。提出了ABC-BP神经网络现场应用思路,构建了地层-掘进参数-沉降的关系,进而通过地层参数直接实现对盾构掘进参数和地表变形控制。

基于BP-AdaBoost算法的复杂产品装配制造成熟度等级评估方法

现有复杂产品装配制造成熟度等级评估依赖专家凭经验确定指标权重和指标评分,存在主观性较强、工作量大、耗时长、无法传承评价实例所蕴含的知识等问题。为了提高复杂产品装配制造成熟度等级评估的效率以及客观性,利用成熟度等级评价实例数据,研究基于BP人工神经网络和AdaBoost算法的制造成熟度等级评估方法。构建复杂产品装配制造成熟度评价指标体系,给出基于模糊评价法和隶属函数的评价指标及成熟度等级达成度量化方法,建立基于BP神经网络的复杂产品装配制造成熟度等级评估模型,并使用AdaBoost算法优化成熟度等级评估BP神经网络模型。采用复杂产品分系统装配制造成熟度评价数据集对评估模型进行训练和实验,分析BP-AdaBoost的评估结果,获得最优评价模型。实验结果表明,基于BP-AdaBoost算法的复杂产品装配制造成熟度等级评估方法具有较好的可靠性与准确度。

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

基于CABC-BP模型的供热管网泄漏诊断研究

针对人工蜂群算法(artificial bee colony algorithm,ABC)在供热管网泄漏检测中收敛速度慢,且易陷入局部最优解的问题,利用混沌算法遍历性、规则性的特点,采用Logistic混沌映射对人工蜂群算法初始蜜源的选择策略和侦查蜂的搜索策略进行优化改进,提出了一种基于混沌蜂群算法(chaotic artificial bee colony algorithm,CABC)优化BP神经网络的供热管网泄漏诊断模型。通过对4个标准测试函数进行仿真验证,并与ABC算法相比,验证了其可行性。将CABC算法用于供热管网泄漏故障诊断中,采用该算法优化BP神经网络的权值和阈值,利用BP神经网络自学习的特点确定泄漏的具体管段以及泄漏点具体位置。实验结果显示,相对于ABC-BP模型和传统BP模型,CABC-BP模型对供热管网泄漏诊断精度显著提高,一级网络诊断准确率为98.33%,二级网络诊断准确率为95.83%,诊断误差均在5%以内。

基于改进AIA-BP算法的煤矿供电线路故障类型识别方法

为了提升煤矿电力系统中供电线路故障识别方法的准确率,提出一种基于小波变换与灰色AIA-BP的煤矿供电线路故障类型识别方法。采用小波变换分析对电力暂态信号进行故障特征提取;引入灰色关联度法对人工免疫算法(AIA)进行改进,并对BP神经网络的的权值与阈值进行了优化,建立灰色AIA-BP故障识别算法模型。仿真结果表明,灰色AIA-BP模型的识别准确率高出传统AIA-BP模型11.37%,故障识别误差率相较于单一BP神经网络模型降低了6.47%,验证了所提方案能够有效地提高供电线路的故障诊断精度;提出模型的平均识别准确率为96.81%,相较于GA-SVM模型和RBF模型提升了14.32%和21.99%,可以对供电线路故障类型进行精确识别判断。

Simulation of Wave Forces on A Semi-Circular Breakwater Using Multilayer Feed Forward Network

In this paper, the Artificial Neural Network (ANN) is used to study the wave forces on a semi-circular breakwater. The process of establishing the network model for a specific physical problem is presented. Networks with double implicit layers have been studied by numerical experiments. 117 sets of experimental data are used to train and test the ANN. According to the results of ANN simulation, this method is proved to have good precision compared with experimental and numerical results.