Mechanical property prediction model of AZ31 magnesium alloys based on an artificial neural network with parameter optimization

The tensile strength, yield strength and elongation of AZ31 magnesium alloys on different annealed conditions are tested by mechanical properties experiments. A model of corresponding mechanical properties is built by applying artificial neural network, and it is optimized by a new method,namely all permutations and combinations training of parameters. The results show that the network model has an excellent performance, which is based on optimal parameters obtained from all permutations and combinations training. Compared with traditional model, whose parameters are obtained from conventional heuristic, the improved model has higher average correlation coefficient and lower average error. Therefore, it can predict the mechanical properties of AZ31 magnesium alloy on different annealed conditions more accurately.     

Fast algorithm for apparent resistivity calculation based on back propagation neural networks

To avoid the complex numerical calculation for the electromagnetic field and determine underground abnormality, a neural network based method is proposed. In consideration of turn off transmitter current, the effect of a linear ramp turn off current on transmitter is corrected. The characteristics of transient expression and the traditional calculation algorithm for apparent resistivity are analyzed, and a predigest structure of network is obtained based on the kernel expression. The three layer back propagation(BP) neural network is trained by using sample data in homogeneous half space, and its number in hidden layer was determined. The method proposed is compared with two traditional calculation methods with simulation experiments. The result demonstrates that BP neural network has a high speed of processing data and is useful in explanation of the transient electromagnetic method.     

An artificial immunity based predictive method for neural networks and its applications

A radial basis function (RBF) neural network learning algorithm based on immune recognition was proposed to improve the low forecast precision and the slow convergence speed of such networks. In the algorithm, artificial immunity was used to determine the center and width parameters of the Gauss basis function. The recognized data were regarded as antigens and the compression mapping of antigens were taken as antibodies, i.e., the centers of the hidden layer. The recursion least square algorithm (RLS) was employed to determine the output layer weights. The algorithm improved the convergence speed and precision of the RBF neural networks. The model was applied to the blast furnace of a large iron and steel company. The results show that the model has forecast precision far superior to existing models and requires less training time than they do.     

Cost estimate of power line projects based on grey relational analysis and neural networks

To accurately estimate the cost of a power line project,a method based on grey relational analysis (GRA) and neural networks (NN) is presented and studied. Grey relational analysis technologies are used to analyze the features of the transmission line project and ten main features which affect the project cost most are selected. Then, the main features are used as input neural cell of neural networks, and a model of GRA-ANN is built. To verify the method, the cost data of a 110 kV power construction project are used to train and test the model. Results show the model’s maximum relative error of static investment is 3.72% and the minimum is 1.85%, and its accuracy is high. The LM-BP algorithm and the traditional BP algorithm are used respectively to train the GRA-ANN network, and results show the error declining rate of LM-BP algorithm is faster and the overall error is lower.     

神经网络自变量输入模式的视电阻率求解算法

根据瞬变电磁场理论公式中的响应和自变量之间的关系特点,提出用非线性方程模式的BP神经网络求解电阻率。通过构造单输入单输出网络结构,建立以不同时间点上的电流归一化的感应电压值为输入、视电阻率值为输出的神经网络,来拟合瞬变电磁场的二次涡流曲线。利用数值方法计算出的数据验证该训练网络的精确性,比较了不同算法对训练精度和收敛速度产生的影响。以重庆大学某处的防空洞探测实验为例验证了该算法的有效性,该算法避开具体的复杂电磁场计算或数值反问题计算,从而实现电阻率快速计算,为快速成像准备必要条件。     

基于布谷鸟搜索神经网络的微波加热温度预测模型

微波加热是一种与被加热物直接相互作用的选择性加热方式,具有清洁、节能、减排等特点。针对工业物料作为微波加热负载时,其温度非线性变化的特点,以微波工业加热过程中的多维、海量参数为研究对象,基于泛函接神经网络模型提取样本数据的深度特征,提出了一种基于布谷鸟搜索算法,优化BP神经网络的网络参数,建立了以"数据驱动"为手段微波加热工业物料温度模型。仿真实验结果证明了所提出模型的准确性、实时性。     

Safety evaluation of hazards based on discrete Hopfield neural network

In order to evaluate hazard’s level efficiently and decrease disasters’ influence on the surrounding environment,a safety evaluation index system of hazards is set up first by considering influence factors of personnel,equipment,raw material,technology,and environment. Then,a hazards safety evaluation model is built by combining neural network with safety system engineering theory. Finally,case studies testify the model can evaluate the hazards’ level reasonably and objectively.     

A method for evolving the architecture and weights of neural networks via gene expression programming

An algorithm for automatic designation of the architecture and the weights of neural networks using gene expression programming (GEP) was presented. The fundamental ideas and procedures of the algorithm were discussed. The algorithm was improved to solve the problems of prematurity and lower variance rate. An application for neural networks designation was given. The experimental results indicate that the proposed GEP approach may evolve the architecture of neural network, and can obtain the weights more precisely. Compared to other conventional evolutional algorithms, GEP shows faster convergence.     

Fetal electrocardiogram extraction based on radial basis function neural networks

A novel method for extracting fetal electrocardiogram (FECG) from the abdominal composite signal of a pregnant woman is proposed. The maternal component in the abdominal electrocardiogram (ECG) signal is a nonlinearly transformed version of the mother's ECG (MECG). This nonlinear relationship was identified using radial basis function (RBF) neural networks. The FECG is extracted by subtracting the nonlinearly transformed version of the MECG from the abdominal ECG signal. The baseline shift and noise in the FECG are suppressed by wavelet packet denoising technique. Experimental results obtained from the actual ECG signals demonstrate the effectiveness of the proposed method in extracting FECG even when it is totally embedded within the maternal(QRS) complex.     

Modeling and simulation of the conducted electromagnetic interference for permanent magnet direct current motors

A simulation model for evaluation of conducted electromagnetic interference(EMI) is developed based on analysis of the mechanism giving rise to conducted EMI. The simulation model includes high frequency effects associated with stray distributed parameter     

带锚FRP受剪加固梁非剥离破坏模式BP网络预测

对带可靠锚固FRP受剪加固混凝土梁的非剥离剪切破坏模式做了细化分类,即包括FRP断裂控制的破坏、受压区混凝土（达到极限应力状态）压碎控制的破坏、FRP断裂与混凝土压碎同步发生的界限破坏等3种模式;利用BP神经网络建立了带锚纤维受剪加固梁破坏模式的智能预测模型,与31根非剥离破坏加固梁试验的对比结果显示：模型总体精度达到90%,说明建立的破坏模式网络预测模型适用于带锚纤维受剪加固梁非剥离剪切破坏模式的判别。     

Thermal process model identification based on radial basisfunction neural networks

In order to accurately reflect the dynamic behavior and realize the whole optimal control of the thermal process, a novel modeling method of the RBF NN (Radial Basis Function Neural Networks) model is proposed to build nonlinear model. This method is based on entropy clustering and competitive learning algorithm, combined with nonlinear autoregressive moving average (NARMA) model to identify the RBF NN stucture, and the power vector is gotten by the least square algorithm. Two simulation experiments show that the proposed method of the identification based on NARMA model and RBF NN can accurately describe the non linearity of the process and has less hidden nodes.     

On-demand routing algorithm based on load balancing and mobility prediction in wireless mobile Ad hoc network

The traditional routing protocol for wireless mobile ad hoc networks is unable to achieve balanced energy consumption and could not adapt to the dynamic topology changes well.A novel on-demand rooting algorithm is proposed based on load balancing and mobility prediction.The proposed rooting algorithm excludes the unstable links in routing discovery,and allows the node with more energy forward the routing request packet preferentially.In addition,it adopts the active local routing recovery strategy by predicting the link connection time,and finishes the repair work before the link being actually failure.The simulation experiments demonstrate that,comparing with the traditional AODV protocol,with slight increase of the rooting control overhead,the proposed algorithm increases the average packet delivery ratio,decreases the average end-to-end delay of the data packets,and achieves load balancing in the network and prolong the life-span of the network,which shows the proposed algorithm is highly practical.     

Cost Estimation of Transmission Line Based on Artificial Neural Network

Based on the structure of cost factor in transmission lines project and the characteristic of neural networks, the paper shows up using artificial neural networks to estimate the cost of project and provide a method to investigate them. By analyzing the structure of project cost and influence factor, the paper builds both input and output neural cell for cost of transmission line project. The paper builds the model of artificial neural networks and exports the steps of the algorithmic. The project sample is used in history to train and test the model. At last, the model gets satisfactory result and meets the investigation requirement of engineering budgetary estimation. Therefore, the paper provides an impersonality and quick investigation method for budgetary estimation of power projects.     

Lossless compression of hyper-spectral interference image based on principal-modulated prediction

A lossless compression algorithm of hyper-spectral interference image based on principal-modulated prediction is proposed. Hyper-spectral interference images are divided into the space direction and the optical path difference(OPD) direction. In the space direction,a principal component prediction algorithm is used to reduce the inter-frame redundancies. And a modulated component prediction is used to reduce the spectral redundancies in the OPD direction. A two-step prediction algorithm is proposed for the principal component prediction. In the first step of prediction,a four order predictor is used to obtain a prediction reference value. In the second step,an 8-level lookup tables’ prediction algorithm is proposed and used to obtain the real-prediction. Then the final prediction is obtained through comparison between the real value and the reference prediction. A linearity prediction is used to obtain modulation prediction frame in the modulated component prediction. Finally,the final prediction frame is obtained through comparison between the principal component frame and the modulated component prediction frame. And the residual frame is obtained,which is encoded by an entropy coder. The experiments results show that the average compression ratio of proposed compression algorithm is reached to 3.05 bpp. Compared with traditional approaches,the proposed method can improve the average compression ratio by 0.14~2.94 bpp. They effectively improve the lossless compression ratio for hyper-spectral image lossless compression.     

Applications of the Fuzzy Neural Networks to Short term Load Forecasting

This paper presents a fuzzy neural network approach to short term load forecasting.It can predict the hourly loads for next day or next week with the fuzzy information.The practical examples have proved the efficiencies of the proposed approach.     

Research on immunization strategies based on independent sets of complex networks

According to the concept of independent set, this paper proposes a new immunization strategy of complex networks which immunizes the nodes in an independent set with maximum degree, called targeted immunization in the independent set. When the total degree of immune nodes in independent set is equal to the total degree of targeted immunization nodes in the whole network, the immunization strategy of independent set is more effective than the targeted immunization strategy of the whole network. Based on the network structure, this paper explains the reason. Comparing the random immunization of independent sets with the random immunization in the whole network, test results indicate that more nodes do not be removed with high degree for the random immunization of independent sets. For classic susceptible-infected(SI) model, all nodes in the network are only two states: susceptible or infected. In the study of immunization strategies, comparing SI model with SIR, SIS models. The use of SI model will be more favorable.     

Beam pumping process modeling and parameters optimization based on generalized regression neural networks for energy conservation

This paper presents a data-mining-based beam pumping unit process modeling and parameters optimization method to solve the problem of inefficiency and energy-intensive of beam pumping unit. The ideality of process parameters is one of the main factors influencing system efficiency and energy consumption, while the effectiveness of mode plays a key role in process parameters choosing. Beam pumping unit system is a complicated nonlinear system, and is hardly to be precisely described by precise mathematical models. Generalized regression neural network (GRNN), which is powerful in nonlinear mapping and generalization, is suitable for nonlinear systems. Therefore, GRNN is proposed to model the beam pumping unit in this paper, and the experimental results show that the fitness is good. Then the trained model is applied to optimize the decision parameters by vector evaluated particle swarm optimization based on Pareto (VEPSO-BP), and at last the resulting parameters are applied to the production. Experimental results show that after using the optimal parameters, the efficiencies and energy consumptions increase more than 6.6% and decrease more than 4.1% respectively, which illustrates the feasibility and effectiveness of the proposed method.