基于BP神经网络的TA15钛合金本构关系建立

本构关系体现了材料在热态塑性加工过程中对热力参数的动态响应,关系到有限元模拟的准确性与精度。文章以TA15钛合金等温压缩实验数据为基础,构造一个3×10×10×1四层BP神经网络结构形式的本构关系模型,采用Bayesian规则化调整法训练网络以提高网络的泛化能力。预测结果、外推结果和实验结果对比表明,利用Bayesian规则化调整法训练的BP神经网络结构形式的TA15钛合金本构关系能够描述其高温变形力学行为,适用于热变形过程的数值模拟。     

Effects of Collinearity on Combining Neural Networks

Collinearity or linear dependency among a number of estimators may pose a serious problem when combining these estimators. The corresponding outputs of a number of neural networks NNs , which are trained to approximate the same quantity or quantities , may be highly correlated. Thus, the estimation of the optimal weights for combining such networks may be subjected to the harmful effects of collinearity, which results in a final model with inferior generalizations ability compared with the individual networks. In this paper, we investigate the harmful effects of collinearity on the estimation of the optimal weights for combining a number on NNs. We discuss an approach for selecting the component networks in order to improve the generalization ability of the combined model. Our experimental results demonstrate significant improvements in the generalization ability of a combined model as a result of the proper selection of the component networks. The approximation accuracy of the combined model is compared with two common alternatives: the apparent best network or the simple average of the corresponding outputs of the networks.     

基于伪标签的弱监督迁移学习模型

针对目标域标记数据少导致迁移模型泛化能力差的问题,提出基于伪标签的半监督迁移学习模型WSTLPL.卷积神经网络用于学习原始振动数据的可迁移特征,用源域数据预训练网络;利用该网络预测目标域数据类别,将分类概率最大的类标签作为数据的伪标签.根据域自适应和伪标签学习的正则化项,对神经网络的参数施加约束,以减少学习到的可迁移特征的分布差异.结果表明:与现有诊断模型相比,该迁移模型的准确率更高.     

基于BP神经网络的置氢TC21合金力学性能预测

基于神经网络的非线性映射和泛化能力,采用人工神经网络方法,建立了置氢TC21合金力学性能预测的BP神经网络模型。模型的输入参数包括高温拉伸试验温度和置氢含量,输出参数为合金的常用力学性能指标,即抗拉强度和屈服强度。通过检验样本验证了ANN模型的准确性。结果表明:该模型具有容错性好、通用性强等优点,可以预测置氢TC21合金在不同拉伸温度和不同置氢含量下的机械性能。同时,将神经网络技术应用于材料制备工艺设计领域,可以明显地提高工艺设计效率,缩短实验周期。     

Extraction,Insertion and Refinement of Symbolic Rules in Dynamically Driven Recurrent Neural Networks

Recurrent neural networks readily process, learn and generate temporal sequences. In addition, they have been shown to have impressive computational power. Recurrent neural networks can be trained with symbolic string examples encoded as temporal sequences to behave like sequential finite slate recognizers. We discuss methods for extracting, inserting and refining symbolic grammatical rules for recurrent networks. This paper discusses various issues: how rules are inserted into recurrent networks, how they affect training and generalization, and how those rules can be checked and corrected. The capability of exchanging information between a symbolic representation (grammatical rules)and a connectionist representation (trained weights) has interesting implications. After partially known rules are inserted, recurrent networks can be trained to preserve inserted rules that were correct and to correct through training inserted rules that were ‘incorrec’—rules inconsistent with the training data.     

Generalization in Sigma-Pi Networks

In this paper, we investigate generalization in supervised feedforward Sigma-pi nets with particular reference to means of augmentation of generalization of the network for specific tasks. The work was initiated because logical (digital) neural networks of this type do not function in the same manner as the more normal semi-linear unit, hence the general principle behind Sigma-pi networks generalization required examination, to enable one to put forward means of augmenting their generalization abilities. The paper studies four methods, two of which are novel methodologies for enhancing Sigma-pi networks generalization abilities. The networks are hardware realizable and the Sigma-pi units are logical (digital) nodes that respond to their input patterns in addressable locations, the locations (site-values) then define the probability of the output being a logical ‘1’. In this paper, we evaluate the performance of Sigma-pi nets with perceptual problems (in pattern recognition). This was carried out by comparative studies, to evaluate how each of the methodologies improved the performance of these networks on previously unseen stimuli.     

基于BP和RBF神经网络的表面质量预测研究

目的 研究RBF和BP神经网络在铣削加工中的作用，实现对铣削加工质量的预测，改善铣削性能。方法 对环形铣刀与常用的球形铣刀进行对比，然后基于MATLAB平台，建立以铣削速度、进给量和铣削深度为输入参数，表面粗糙度为输出参数的RBF神经网络模型。通过大量的试验数据对RBF神经网络模型进行训练，然后再用训练好的RBF神经网络模型预测表面粗糙度，将预测值与实测值进行比较，验证RBF神经网络的预测性能。将训练好的BP神经网络模型与RBF神经网络所建模型的预测结果进行比较。结果 发现用RBF方法预测的表面粗糙度相对误差的绝对值不超过6%，最大误差为0.056 098，平均误差为0.022 277，而BP方法的最大误差为0.074 947，平均误差为0.036 578。结论 环形铣刀加工质量更好。RBF神经网络的预测精度较高，具有比BP神经网络更优的预测能力，且拥有建模时间短、收敛速度高、训练过程稳定以及学习速度快等优点，能有效进行铣削质量预测。     

STUDY ON CONSTITUTIVE RELATIONSHIP OF Ti-5Al-2Sn-2Zr-4Cr-4Mo ALLOY AND SIMULATION OF HOT FORMING PROCESSES

.~theSofar,finiteelementmethodhaswidelybeenusedinmetalfoeingprocesses,whichcanhelptoshortenthedevelopmentcycleandreducetheproductcosts.ConstitutiverelationshipisabridgebetweenthedefonnationbehaViorofmaterialsandallkindsOfthermomechanicalparameters,anditisalsoapresupPOsitiontothesimulationOfmetaldeformationprocessesbyusingfiniteelementmethod.Fwhermore,itisusuallynon--linearandcomplex,owingtoavallationinstructUredabingplasticdefonnation,Particularlyoccultinginsupendloys.FOrmanyyears,researche…     

基于模糊神经网络的逆变点焊电源恒电流控制设计及仿真

推导了逆变点焊过程控制模型,并构建了逆变点焊模糊神经网络恒电流控制系统结构.根据该模型采用先正弦后恒定输入的方法对模糊神经网络(FNN)进行分段离线学习,提高网络的泛化能力和自适应能力.在线控制时,利用训练后的网络仅做正向模糊计算,输出逆变桥开关管占空比改变量的方法保证逆变器恒电流输出.最后使用MATLAB高级语言编程,完成了整个系统的仿真实验.仿真结果表明:分段训练后的FNN使用该方法可以实现逆变点焊电源的恒电流控制.     

Application of neural networks to an expert system for cold forging

The technique of neural networks is applied to an expert system for cold forging in order to increase the consultation speed and to provide more reliable results. A three-layer neural network is used and the back-propagation algorithm is employed to train the network.

By utilizing the ability of pattern recognition of neural networks, a system is constructed to relate the shapes of rotationally symmetric products to their forming methods. The cross-sectional shapes of the products which can be formed by one blow are transformed into 16 × 16 black and white points and are given to the input layer. After learning about 23 products, the system is able to determine the forming methods for the products which are exactly the same or slightly different from the products used in the network training. To exploit the self-learning ability, the neural networks are applied to the prediction of the most probable number of forming steps, from information about the complexity of the product shape and the materials of the die and billet, and also to the generation of rules from the knowledge acquired from an FEM simulation. It is found that the prediction of the most probable number of forming steps can be made successfully and that the FEM results are represented better by the neural networks than by the statistical methods.     

基于改进型S算子BP神经网络的钢材大气腐蚀影响因子评估模型

通过对双曲正切-S算子的改进,提出了一种用于钢的大气腐蚀影响因子评估的BP神经网络模型,采用零均值标准化使输入数据符合模型要求,引入贝叶斯正则化算法解决了小样本泛化问题。仿真试验表明,该模型能在无任何先验知识的情况下较好的反映诸因子对大气腐蚀的影响。     

复合正交柔性神经网络及其应用

针对目前神经网络所存在的不足，提出一种带参数的单极性Sigmoid函数的柔性复合正交神经网络，并给出相应的参数学习算法，这种柔性复合正交神经网络不仅扩大了网络辨识模型的能力与学习适应性，而且算法简单，学习收敛速度快，有线性，非线性逼近精度高等优异特性。以模型辨识作为应用实例，仿真结果表明，其算法是有效的，柔性神经网络能提高正交神经网络的性能。     

Actively Searching for an Effective Neural Network Ensemble

A neural network NN ensemble is a very successful technique where the outputs of a set of separately trained NNs are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm called ADDEMUP that uses genetic algorithms to search explicitly for a highly diverse set of accurate trained networks. ADDEMUP works by first creating an initial population, then uses genetic operators to create new networks continually, keeping the set of networks that are highly accurate while disagreeing with each other as much as possible. Experiments on four real-world domains show that ADDEMUP is able to generate a set of trained networks that is more accurate than several existing ensemble approaches. Experiments also show ADDEMUP is able to incorporate prior knowledge effectively, if available, to improve the quality of its ensemble.     

Distributed representations for extended syntactic transformation

This paper shows how the choice of representation substantially affects the generalization performance of connectionistnetworks. The starting point is Chalmers' simulations involving structure-sensitive processing. Chalmers argued that a connectionist network could handle structure sensitive processing without the use of syntactically structured representations. He trained a connectionist architecture to encode/decode distributed representations for simple sentences. These distributed representations were then holistically transformed such that active sentences were transformed into their passive counterpart. However, he noted that the recursive auto-associative memory (RAAM), which was used to encode and decode distributed representations for the structures, exhibited only a limited ability to generalize when trained to encode/decode a randomly selected sample of the total corpus. When the RAAM was trained to encode/decode all sentences, and a separate transformation network was trained to make some active-passive transformations of the RAAMencoded sentences, the transformation network demonstrated perfect generalization on the remaining test sentences. It is argued here that the main reason for the limited generalization is not the ability of the RAAM architecture per se, but the choice of representation for the tokens used. This paper shows that 100% generalization can be achieved for Chalmers' original set up (i.e. using only 30% of the total corpus for training). The key to this success is to use distributed representations for the tokens (capturing different characteristics for differentclasses of tokens, e.g. verbs or nouns).     

Direct back propagation neural dynamic programming-based particle swarm optimisation

In this paper, we introduce direct back propagation (BP) neural dynamic programming (NDP) into particle swarm optimisation (PSO). Thus, a direct BP NDP inspired PSO algorithm, which we call NDPSO, is proposed. In NDPSO, since direct BP NDP belongs to the class of heuristic dynamic programming algorithms based on model-based adaptive critic designs and often serves as an online learning control paradigm, critic BP neural network is trained to optimise a total reward-to-go objective, namely to balance Bellman's equation, while action BP neural network is used to train the inertia weight, cognitive, and social coefficients so that the critic BP network output can approach an ultimate reward-to-go objective of success. With the collective aid of action-critic BP neural networks, inertia weight, cognitive, and social coefficients become more adaptive. Besides, the NDPSO's mutation mechanism also has greatly improved the dynamic performance of the standard PSO. Empirical experiments are conducted on both unimodal and multimodal benchmark functions. The experimental results demonstrate NDPSO's effectiveness and superiority to many other PSO variants on solving most multimodal problems.     

电液伺服系统神经网络建模研究

本文讨论了非线性系统的神经网络建模的有关问题。提出了对神经网络进行泛化训练及对网络的泛化能力进行定量分析的方法。结合文中提出的一种简化动态网络，通过对一个电液伺服系统的仿真，验证了本文提出的方法是可行的。     

Extracting dimensional information from steel reinforcing bars in concrete using neural networks trained on data from an inductive sensor

A new method is presented for extracting dimensional information from steel bars using images generated by an inductive sensor. The technique is based on the application of two feedforward backpropagation neural networks; one to estimate bar depth and the other to estimate bar diameter. Both of the networks have been trained on a set of data that consists of the peak parameters of six different bars scanned at 41 different bar depths. These input and target data must be pre-processed to obtain a good network generalisation. By testing the two networks with a completely different set of data, accurate performance has been obtained. Real, two-dimensional scan data have then been applied to both of the networks and the bar dimensional parameters have been extracted successfully. The advantage of the neural network method for extracting information is that it continues to operate reliably for very deep bars, for which the signal strength is severely attenuated and manifests a poor signal-to-noise ratio. Depth and diameter measurements have been obtained for bars located down to 58 mm, with errors that satisfy the requirements of the BS 1881 standard. At a depth of 40 mm, these measurements yield an error of ±4%, and this decreases as the depth reduces; in other words, the extracted bar diameter is within the requirements of the DIN 488 standard.     

汽车踏板横梁翻边过程中的回弹预测

以汽车踏板横梁为研究对象,结合数值模拟技术与GRNN神经网络对零件翻边过程中的回弹情况进行预测。首先采用Autoform对踏板横梁翻边过程进行模拟,并与相同参数下实际零件回弹角进行对比,验证模拟结果的准确性和可替代性。再通过设计正交试验获取不同参数组合下各检测点的回弹角数据作为样本数据,并在MATLAB中对GRNN神经网络进行训练。为保证预测精度,设置多组光滑因子进行训练,发现光滑因子为0.1时,网络具有最优的逼近性能和预测性能,并作为最终网络模型进行检验。通过预测结果与真实结果进行对比,发现预测误差最大为4.3%,满足生产要求。研究表明,GRNN神经网络对板料翻边回弹预测既具有较高效率,又具有较高的精度。     

Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel

Laser assisted oxygen cutting (LASOX) process is an efficient method for cutting thick mild steel plates compared to conventional laser cutting process. However, scanty information is available as to modeling of the process. The paper presents an optimized SA-ANN model of artificial neural network (ANN) and simulated annealing (SA) to predict and optimize cutting quality of LASOX cutting process of mild steel plates. Optimization of SA-ANN parameters is carried out first where the ANN architecture and initial temperature for SA are optimized. The optimized ANN architecture is further trained using single hidden layer back propagation neural network (BPNN) with Bayesian regularization (BR). The trained ANN is then used to evaluate the objective function during optimization with SA. Experimental dataset employed for the purpose consists of input cutting parameters comprising laser power, cutting speed, gas pressure and stand-off distance while the resulting cutting quality is represented by heat affected zone (HAZ) width, kerf width and surface roughness. Results indicate that the SA-ANN model can predict the optimized output with reasonably good accuracy (around 3%). The proposed approach can be extended for prediction and optimization of operational parameters with reasonable accuracy for any experimental dataset.