Manufacturing features recognition using backpropagation neural networks

A backpropagation neural network (BPN) is applied to the problem of feature recognition from a boundary representation (B-rep) solid model to facilitate process planning of manufactured products. It is based on the use of the face complexity code to represent the features and a neural network for the analysis of the recognition. The face complexity code is a measure of the face complexity of a feature based on the convexity or concavity of the surrounding geometry. The codes for various features are fed to the network for analysis. A backpropagation network is implemented for recognition of features and tested on published results to measure its performance. Any two or more features having significant differences in face complexity codes were used as exemplars for training the network. A new feature presented to the network is associated with one of the existing clusters, if they are similar, or the network creates a new cluster, if otherwise. Experimental results show that the network was consistent in recognizing features, hence is appropriate for application to the problem of feature recognition in automated manufacturing environment.     

Hybrid neural network and genetic algorithm based machining feature recognition

In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.     

基于双流对称特征融合网络模型的海洋船舶目标识别

海洋船舶目标识别在民用和军事领域有着重要的战略意义, 本文针对可见光图像和红外图像提出了一种 基于注意力机制的双流对称特征融合网络模型, 以提升复杂感知环境下船舶目标综合识别性能. 该模型利用双流对 称网络并行提取可见光和红外图像特征, 通过构建基于级联平均融合的多级融合层, 有效地利用可见光和红外两种 模态的互补信息获取更加全面的船舶特征描述. 同时将空间注意力机制引入特征融合模块, 增强融合特征图中关 键区域的响应, 进一步提升模型整体识别性能. 在VAIS实际数据集上进行系列实验证明了该模型的有效性, 其识别 精确度能达到87.24%, 综合性能显著优于现有方法.     

一种ART2神经网络的改进算法

传统的ART2神经网络由于预处理阶段的归一化，易将重要但幅值较小的分量作为噪声清除，造成在分类中丢失重要信息，同时还存在模式漂移的不足，分析产生这些不足的原因，并基于去单位化以及类内样本与类中心的距离不同而对类中心偏移产生不同影响的思想，对传统的ART2神经网络算法进行了改进。对一组渐变数据的测试表明，改进后的网络有效改善了模式漂移现象。同时，改进的ART2神经网络在核辐射场数据处理分类中有一定的实用价值。     

A RT 2神经网络的一种改进

传统A RT 2神经网络在聚类过程中模式的匹配度量仅仅与模式的相位信息相关,这种匹配度量忽略了模式的幅度信息的作用,在对相位信息相同而幅度信息不同的两个簇进行聚类时,效果很差;同时,它还存在输入域限制的问题。针对这些不足之处,提出了一种改进的A RT 2神经网络,在输入模式进入网络学习过程中,保存其幅值信息,放宽对负实数的非线性转换,并考虑输入模式到各个簇的中心点的最短距离,同时增加一个阈值对离群点进行判定,消除了离群点对聚类结果的影响。实验验证,改进的A RT 2网络在对相同相位的两个簇聚类时,性能明显优于传统的A RT 2网络。     

一种改进的ART2网络学习算法

分析了现有ART2网络存在的问题,提出了一种改进的ART2算法。该算法首先利用样本数据自身来初始化权值,然后按照同一类中的数据点到其聚类中心的距离之和越小(即类内偏差越小),聚类效果越好的原则来设计特征表示场和类别表示场之间的权值修正公式,最后通过比较输入样本和聚类中心的模来有效地利用模式的幅度信息。分析证明了该算法不仅能有效解决模式漂移问题、充分利用幅度信息,而且能提高聚类速度。     

Large machine-part family formation utilizing a parallel ART1 neural network

The binary adaptive resonance (ART1) neural network algorithm has been successfully implemented in the past for the classifying and grouping of similar vectors from a machine-part matrix. A modified ART1 paradigm which reorders the input vectors, along with a modified procedure for storing a group's representation vectors, has proven successful in both speed and functionality in comparison to former techniques. This paradigm has been adapted and implemented on a neuro-computer utilizing 256 processors which allows the computer to take advantage of the inherent parallelism of the ART1 algorithm. The parallel implementation results in tremendous improvements in the speed of the machine-part matrix optimization. The machine-part matrix was initially limited to 65,536 elements (256×256) which is a consequence of the maximum number of processors within the parallel computer. The restructuring and modification of the parallel implementation has allowed the number of matrix elements to increase well beyond their previous limits. Comparisons of the modified structure with both the serial algorithm and the initial parallel implementation are made. The advantages of using a neural network approach in this case are discussed.     

Spectral recognition using a modified Eckhorn neural network model

Studies of the visual cortex of the cat highlight the role of temporal processing using synchronous oscillations for object identification. In this paper, the original neural network model of Eckhorn has been modified according to the proposal of Johnson and others and used for spectral recognition. The method developed turns out to be a much simpler, faster and elegant way of spectral recognition than reported elsewhere.     

模糊ART神经网络的识别算法及其应用

提出并设计了模糊ART神经网络的结构、学习规则和识别算法.为了把该算法应用于人脸识别,定义了相似函数和匹配搜索方法,通过向量柱状图提取人脸特征,并用模糊ART神经网络对向量柱状图生成的特征向量进行识别.仿真实验结果表明,对于快速学习和非快速学习,不同的人具有不同的识别率,各有不同的警戒参数值可以使神经网络到达在线最大识别率82.25%和86%.     

A new approach for automated parts recognition using time series analysisand neural networks

This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.     

基于小波变换和ART网络的手写数字识别

由于小波变换能有效地提取字符的结构特征,自适应共振(ART)网络有很好的学习能力。本文将二者结合起来,用小波变换抽取特征、用自适应共振ART网络作模式分类器来识别手写数字。实验证明该方法有很高的识别率,能够有效地进行手写数字的分类,可以满足实际应用。     

An on-line adaptive neural network for speech recognition

In this paper, we present an on-line learning neural network model, Dynamic Recognition Neural Network (DRNN), for real-time speech recognition. The property of accumulative learning of the DRNN makes it very suitable for real-time speech recognition with on-line learning. A comparison between the DRNN and Hidden Markov Model (HMM) shows that the computational complexity of the former is lower than that of the latter in both training and recognition. Encouraging results are obtained when the DRNN is tested on a BUPT digit database (Mandarin) and on the on-line learning of twenty isolated English computer command words.     

基于特征加权的Category ART网络及应用

特征加权是特征选择的一般情况，它能更加细致地区分特征对结果影响的程度，往往能够获得比特征选择更好的或者至少相等的性能。该文采用自适应遗传算法来优化Category ART网络的特征权值，提出了一种改进的Category ART网络FWART。在UCI标准数据集上的实验表明，FWART网络获得了比Category ART网络更好的泛化能力。将该网络应用在地震震型预报上，取得了很好的预报效果。     

Advanced self-organizing polynomial neural network

In this paper, we introduce a concept of advanced self-organizing polynomial neural network (Adv_SOPNN). The SOPNN is a flexible neural architecture whose structure is developed through a modeling process. But the SOPNN has a fatal drawback; it cannot be constructed for nonlinear systems with few input variables. To relax this limitation of the conventional SOPNN, we combine a fuzzy system and neural networks with the SOPNN. Input variables are partitioned into several subspaces by the fuzzy system or neural network, and these subspaces are utilized as new input variables to the SOPNN architecture. Two types of the advanced SOPNN are obtained by combining not only the fuzzy rules of a fuzzy system with SOPNN but also the nodes in a hidden layer of neural networks with SOPNN into one methodology. The proposed method is applied to the nonlinear system with two inputs, which cannot be identified by conventional SOPNN to show the performance of the advanced SOPNN. The results show that the proposed method is efficient for systems with limited data set and a few input variables and much more accurate than other modeling methods with respect to identification error.     

基于神经网络的目标识别模型验证方法研究

针对多传感器目标识别仿真模型的验证问题,提出了一种基于多神经网络的“分层有序”的模型验证方法。该方法利用神经网络的自组织和自学习能力,通过对各种目标识别模型关键行为特性的学习,将实际系统行为归类为其中的一种模型,从而对模型的可信性做出评估。仿真结果进一步说明了该方法的可行性和有效性。     

Feature extraction and image segmentation using self-organizing networks

Feature extraction and image segmentation (FEIS) are two primary goals of almost all image-understanding systems. They are also the issues at which we look in this paper. We think of FEIS as a multilevel process of grouping and describing at each level. We emphasize the importance of grouping during this process because we believe that many features and events in real images are only perceived by combining weak evidence of several organized pixels or other low-level features. To realize FEIS based on this formulation, we must deal with such problems as how to discover grouping rules, how to develop grouping systems to integrate grouping rules, how to embed grouping processes into FEIS systems, and how to evaluate the quality of extracted features at various levels. We use self-organizing networks to develop grouping systems that take the organization of human visual perception into consideration. We demonstrate our approach by solving two concrete problems: extracting linear features in digital images and partitioning color images into regions. We present the results of experiments on real images.     

基于ART-2神经网络及不变矩特征的图像模式识别

将图像进行预处理并提取图像的特征,计算出图像的不变矩,利用ART-2神经网络完成了对图像的模式识别。通过实验证明ART-2神经网络具有较高的识别率,并很好地解决了神经网络在模式识别中面对识别对象出现新模式时,网络的可塑性与稳定性的矛盾。     

On-line recognition of Korean characters using ART neural network and hidden Markov model

This paper proposes an efficient method for on-line recognition of cursive Korean characters. The recognition of cursive strokes and the representation of a large character set are important determinants in the recognition rate of Korean characters. To deal with cursive strokes, we classify them automatically by using an ART-2 neural network. This neural network has the advantage of assembling similar patterns together to form classes in a self-organized manner. To deal with the large character set, we construct a character recognition model by using the hidden Markov model (HMM), which has the advantages of providing an explicit representation of time-varying vector sequence and probabilistic interpretation. Probabilistic parameters of the HMM are initialized using the combination rule for Korean characters and a set of primitive strokes that are classified by the ART stroke classifier, and trained with sample data. This is an efficient means of representing all the 11,172 possible Korean characters. We tested the model on 7500 on-line cursive Korean characters and it proved to perform well in recognition rate and speed.     

基于离散Hopfield神经网络的数字识别实现

介绍了离散Hopfield神经网络的基本概念;以MATLAB为工具,根据Hopfield神经网络的相关知识,设计了一个具有联想记忆功能的离散型Hopfield神经网络,并给出了设计思路、设计步骤和测试结果。实验结果表明,通过联想记忆,对于带有一定噪声的数字点阵,Hopfield网络可以正确地进行识别,且当噪声强度为0．1时的识别效果较好。     

Speech recognition using pulse-coupled neural networks with a radial basis function

We have developed a novel pulse-coupled neural network (PCNN) for speech recognition. One of the advantages of the PCNN is in its biologically based neural dynamic structure using feedback connections. To recall the memorized pattern, a radial basis function (RBF) is incorporated into the proposed PCNN. Simulation results show that the PCNN with a RBF can be useful for phoneme recognition. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002