Cascaded linear shift-invariant processors in optical pattern recognition

We study a cascade of linear shift-invariant processing modules (correlators), each augmented with a nonlinear threshold as a means to increase the performance of high-speed optical pattern recognition. This configuration is a special class of multilayer, feed-forward neural networks and has been proposed in the literature as a relatively fast best-guess classifier. However, it seems that, although cascaded correlation has been proposed in a number of specific pattern recognition problems, the importance of the configuration has been largely overlooked. We prove that the cascaded architecture is the exact structure that must be adopted if a multilayer feed-forward neural network is trained to produce a shift-invariant output. In contrast with more generalized multilayer networks, the approach is easily implemented in practice with optical techniques and is therefore ideally suited to the high-speed analysis of large images. We have trained a digital model of the system using a modified backpropagation algorithm with optimization using simulated annealing techniques. The resulting cascade has been applied to a defect recognition problem in the canning industry as a benchmark for comparison against a standard linear correlation filter, the minimum average correlation energy (MACE) filter. We show that the nonlinear performance of the cascade is a significant improvement over that of the linear MACE filter in this case.     

A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds

A simple self-adaptive version of the differential evolution algorithm was applied for simultaneous architectural and parametric optimization of feed-forward neural networks, used to classify the crystalline liquid property of a series of organic compounds. The developed optimization methodology was called self-adaptive differential evolution neural network (SADE-NN) and has the following characteristics: the base vector used is chosen as the best individual in the current population, two differential terms participate in the mutation process, the crossover type is binomial, a simple self-adaptive mechanism is employed to determine the near-optimal control parameters of the algorithm, and the integration of the neural network into the differential evolution algorithm is performed using a direct encoding scheme. It was found that a network with one hidden layer is able to make accurate predictions, indicating that the proposed methodology is efficient and, owing to its flexibility, it can be applied to a large range of problems.     

优化遗传神经网络及其在机械故障诊断中的应用

提出了一种改进的遗传神经网络算法,该算法综合了遗传算法的全局性和神经网络的并行快速性等特点,有利于克服神经网络存在易陷入局部极小和收敛速度慢的问题,达到了优化网络的目的.此算法应用于磨机故障诊断,通过试验得出对故障模式的识别精度较高,具有较好的应用前景.     

Autoignition Temperature Prediction Using an Artificial Neural Network with Particle Swarm Optimization

The autoignition temperatures of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) replacing a standard back-propagation algorithm with particle swarm optimization (PSO). A data set of 250 compounds was used for training the network. The optimal condition of the network was obtained by adjusting various parameters by trial-and-error. The capabilities of the designed network were tested in the prediction of the autoignition temperature of 93 compounds not considered during the training step. The proposed model is shown to be more accurate than those of other published works. The results show that the proposed GCM + ANN + PSO method represent an excellent alternative for the estimation of this property with acceptable accuracy (AARD = 1.7%; AAE = 10K).     

基于结构响应统计特征的神经网络损伤识别方法

提出一种采用结构动态响应的统计特征作为损伤指标的神经网络损伤识别方法,并对其进行了数值模拟和实验验证。首先,通过敏感性分析,分析了采用结构动力响应的统计特征作为损伤指标的可行性;然后数值模拟了一三跨连续梁采用结构位移方差作为损伤指标的神经网络损伤识别过程,其结果表明,经过训练的神经网络可以准确的识别出单损伤和多损伤工况中的损伤位置和损伤程度;最后进行一组两端固定的简支梁模型实验来验证所提出损伤识别方法的有效性。实验结果表明,对于单损伤工况,神经网络可以准确地识别出结构中损伤位置和损伤程度,对于双损伤工况,神经网络可以准确地识别出损伤位置,而损伤程度识别略有偏差。最后得出结论,采用结构动力响应的统计特征作为损伤指标的神经网络损伤识别方法是可靠有效的。     

基于平移不变Zernike矩和模块化神经网络的掌纹识别方法

在对掌纹原始图像进行去噪、分割等预处理之后,利用平移不变的Zernike矩特征矢量（TIZMs）作为掌纹特征建立特征库,根据已知分类信息建立样本集。并将问题分解为多个小规模的两类问题,然后采用模块化神经网络（MNN）作为分类器进行掌纹识别。对香港理工大学的Polyu PalmprintDB数据库中的3200个掌纹进行实验,在响应时间和识别精度等方面获得了很好的结果。     

Assessment of the effect of existing corrosion on the tensile behaviour of magnesium alloy AZ31 using neural networks

A concept has been devised to assess the effect of existing corrosion damage on the residual tensile properties of structural alloys and applied for the magnesium alloy AZ31. The concept based on the use of a radial basis function neural network. An extensive experimental investigation, including metallographic corrosion characterization and mechanical testing of pre-corroded AZ31 magnesium alloy specimens, was carried out to derive the necessary data for the training and the prediction module of the developed neural network model. The proposed concept was exploited to successfully predict: the gradual tensile property degradation of the alloy AZ31 to the results of gradually increasing corrosion damage with increasing corrosion exposure.     

基于自适应模糊逻辑和神经网络的双足机器人控制研究

在双足机器人行走控制中,为了改善系统的行走性能,提出了一种基于RBF神经网络前馈控制的力矩补偿控制方法。该方法将自适应模糊控制和神经网络逆模控制有效地结合起来,利用神经网络来逼近系统的逆动力学模型,提高系统了的控制性能,改善了机器人的行走特性。     

A novel multi-modal medical image fusion method based on shift-invariant shearlet transform

Abstract

Medical image fusion plays an important role in clinical applications, such as image-guided surgery, image-guided radiotherapy, non-invasive diagnosis and treatment planning. Shearlet is a novel multi-scale geometric analysis (MGA) tool proposed recently. In order to overcome the drawback of the shearlet-based fusion methods that the pseudo-Gibbs phenomenon is easily caused around the singularities of the fused image, a new multi-modal medical image fusion method is proposed in shift-invariant shearlet transform domain. First, the original images are decomposed into lowpass sub-bands and highpass sub-bands; then, the lowpass sub-bands and high sub-bands are combined according to the fusion rules, respectively. All the operations are performed in shift-invariant shearlet domain. The final fused image is obtained by directly applying inverse shift-invariant shearlet transform to the fused lowpass sub-bands and highpass sub-bands. Experimental results demonstrate that the proposed method can not only suppress the pseudo-Gibbs phenomenon efficiently, but perform better than the popular wavelet transform-based method, contourlet transform-based method and non-subsampled contourlet transform-based method.     

基于遗传算法和最小二乘支持向量机的织物剪切性能预测

提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.     

基于微分进化神经网络的模拟电路故障诊断

BP神经网络已在模拟电路故障诊断领域得到广泛应用,但BP神经网络存在训练速度慢且容易陷入局部最优的问题.由此,本文提出了一种基于混合变异策略的微分进化改进算法,描述了利用微分进化改进算法进行神经网络权值训练的过程和方法,并将微分进化神经网络用于模拟电路故障诊断,文中还对微分进化神经网络与BP神经网络进行了比较.实验结果表明,微分进化神经网络的训练时间和训练精度均优于BP神经网络,其在模拟电路故障诊断中的准确度比BP神经网络提高了7%.     

斜拉索基于MR阻尼器的神经网络半主动控制

该文利用神经网络强大的学习和非线性拟合能力模拟了MR阻尼器的逆动力性能。为了提高神经网络的计算性及泛化性,采用了Levenberg-Marquardt算法与贝叶斯正规化法相结合的方法。与此同时,利用MR阻尼器的神经网络逆模型,提出了一种新的斜拉索神经网络半主动控制策略。为验证所提控制方法的有效性,针对典型算例进行了数值分析,并将其与LQR主动控制方法进行了比较。得出结论:所提神经网络半主动控制方法是有效的,与LQR主动控制效果相比,效果略差,但相差不大。     

基于向量小波与神经网络的图像融合算法

提出了一种基于向量小波和神经网络的图像融合算法.首先对各源图像进行向量小波变换,根据变换后系数计算出各子块图像的清晰度,选取子块图像部分区域清晰度作为前溃神经网络的训练样本,调整神经网络权重;然后用训练好的神经网络组合融合图像的向量小波系数,对组合后的系数进行一致性校验;最后对该系数进行向量小波逆变换,得到融合图像.仿真实验表明,该算法能够较好地解决多传感器图像融合问题,生成的融合图像效果优于有代表性的图像融合方法.     

基于模糊神经网络的数据融合结构损伤识别方法

为了有效利用结构健康监测系统中的多源传感器数据信息,提高损伤检测与评估的识别正确率,该文通过构造模糊神经网络分类器,提出了一种基于模糊神经网络的数据融合损伤识别方法并将之应用于结构健康诊断中。它先通过数据预处理,提取原始响应信号中的特征参数,接着将之作为模糊神经网络的输入,构造模糊神经网络模型进行识别决策,最后运用数据融合算法,计算出数据融合后的决策结果。为了验证所提方法的有效性,通过一个7自由度的建筑模型,分别用单一模糊神经网络决策器和数据融合损伤识别方法进行了损伤识别和比较。研究结果表明:该文所提方法比单一决策结果更准确、可靠。     

旋转机械故障诊断中的主从混合神经网络模型研究

针对ＢＰ网络在旋转机械故障诊断应用中的不足，借助Ｈｏｐｆｉｅｌｄ网络的优良特性，建立了以反馈式Ｈｏｐｆｉｅｌｄ网络为主控网络、前馈式ＢＰ网络为从网络的主从混合神经网络模型。通过这个网络模型的设计、动力学行为分析、学习算法的描述和测试以及它在旋转机械故障诊断中的应用，结果表明：该网络模型具有收敛速度快、稳定性好、最小系统误差等优点，是一种实现旋转机械故障诊断的优良网络模型。     

基于遗传算法的神经网络被动声呐目标分类研究

被动声呐目标识别系统中目标分类器的设计和训练是一项重要内容，本文设计了目标分类器的神经网络结构，提出了一种用改进的遗传算法训练神经网络分类器的新方法，最后，对海上实录的Ａ，Ｂ，Ｃ三类目标噪声进行了分类识别，实验结果表明基于遗传算法的神经网络分类器比传统的基于ＢＰ算法的神经网络分类源泛化性能有明显提高。     

径向基函数神经网络在多维力传感器标定中的应用

维间耦合是制约多维力传感器测量精度的主要因素,为了克服传统线性标定方法的局限性,利用径向基函数（RBF）神经网络强非线性逼近能力进行了多维腕力传感器的静态标定,并将其与最小二乘法和BP神经网络标定法作了比较。以研制的六维腕力传感器为对象进行了实验,结果表明,采用RBF神经网络对多维腕力传感器标定比用最小二乘线性标定有更高的标定精度,网络训练速度则大大快于BP神经网络。这种新方法具有一定的实用价值。     

基于小波神经网络的蒸汽发生器水位辨识与控制仿真

提出一种基于小波神经网络的控制方法，对蒸汽发生器水位进行控制仿真．该方法利用小波神经网络作为控制系统的辨识器和控制器来构成控制系统．小波神经网络辨识器能更准确逼近非线性对象。小波神经网络控制器能自适应产生最佳的控制规律．仿真结果表明，该方案具有响应快、超调量小、较强抑制干扰能力等良好性能．