基于拉普拉斯卷积网络和SMM分类器的小麦麦粒识别

为了提高小麦麦粒识别的识别率,采用了拉普拉斯卷积网络(Convolution Network Based on Laplacian Eigenmap,LENet)和支持矩阵机(Support Matrix Machines,SMM)分类器相结合的方法对小麦麦粒进行识别。拉普拉斯卷积网络是一种无反馈的轻量型级联卷积神经网络,可以用来提取小麦麦粒的特征,该网络通过拉普拉斯特征映射来学习网络的参数,输出层通过块直方图编码和矩阵化处理实现,最终提取的特征使用SMM分类器进行分类。通过在建立的小麦麦粒图像数据库上的实验表明,该麦粒识别方法要优于一些传统特征提取分类方法,取得了较好的识别效果。     

Pictorial information retrieval using the random neural network

A technique is developed based on the use of a neural network model for performing information retrieval in a pictorial information system. The neural network provides autoassociative memory operation and allows the retrieval of stored symbolic images using erroneous or incomplete information as input. The network used is based on an adaptation of the random neural network model featuring positive and negative nodes and symmetrical behavior of positive and negative signals. The network architecture considered has hierarchical structure and allows two-level operation during learning and recall. An experimental software prototype, including an efficient graphical interface, has been implemented and tested. The performance of the system has been investigated through experiments under several schemes concerning storage and reconstruction of patterns. These schemes are either based on properties of the random network or constitute adaptations of known neural network techniques     

基于多级分类器和神经网络集成的手写体汉字识别

为了提高系统的泛化能力,在分析了当前汉字识别最新发展技术的基础上,提出了一种三级识别策略的汉字识别系统模型.第一级,使用传统的外围特征法将待选字进行粗分;第二级,使用笔划密度特征法进行细分;第三级,使用一种基于球领域模型的神经网络集成算法对结果进行最后的确认.模拟算法证明,它可以更进一步地提高系统的泛化能力.     

A novel and efficient classifier using spiking neural network

The Journal of Supercomputing - Classification plays a crucial role in big data, especially in e-commerce operations. Deep learning (DL) research has become a new means to provide a better solution...     

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.     

在癌症分类中基于分层抽样的神经网络集成算法

在基因表达谱数据的分析中,针对有效合理地选择特征基因集的问题,本文将分层抽样技术引入特征基因选择,提高特征基因集的分类能力。以神经网络作为分量分类器,神经网络集成进行分类预测。并在结肠癌数据集上进行实验,实验结果表明该方法能有效地降低特征基因集选择的复杂性,提高对于未知样本的分类预测效果。     

A polynomial fuzzy neural network for modeling and control

This paper introduces a novel neurofuzzy system based on polynomial fuzzy neural network (PFNN) architecture. A PFNN consists of a set of if-then rules with appropriate membership functions (MFs) whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection is defined to overcome the overfitting problem in the modeling procedure. For a performance assessment of the PFNN inference system, two well-known problems are employed for a comparison with other methods. The results of these comparisons show that the PFNN inference system out-performs the other methods and exhibits robustness characteristics. This work was presented in part at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Neuron selection for RBF neural network classifier based on data structure preserving criterion

The central problem in training a radial basis function neural network is the selection of hidden layer neurons. In this paper, we propose to select hidden layer neurons based on data structure preserving criterion. Data structure denotes relative location of samples in the high-dimensional space. By preserving the data structure of samples including those that are close to separation boundaries between different classes, the neuron subset selected retains the separation margin underlying the full set of hidden layer neurons. As a direct result, the network obtained tends to generalize well.     

A neural gain scheduling network controller for nonholonomicsystems

We propose a neural gain scheduling network controller (NGSNC) to improve the gain scheduling controller for nonholonomic systems. We derive the neural networks that can approximate the gain scheduling controller arbitrarily well when the sampling frequency satisfies the sampling theorem. We also show that the NGSNC is independent of the sampling time. The proposed NGSNC has the following important properties: 1) same performance as the continuous-parameter gain scheduling controller; 2) less computing time than the continuous-parameter gain scheduling controller; 3) good robustness against the sampling intervals; and 4) straightforward stability analysis. We then show that some of nonholonomic systems can be converted to equivalent linear parameter-varying systems. As a result, the NGSNC can stabilize nonholonomic systems     

基于神经网络的基因分类器

随着人类基因组计划的成果和生物信息学的发展,DNA、RNA和蛋白质的数据量空前增长。从这些生物数据中挖掘出有用的知识对于基因组处理尤为重要。其中,通过对DNA序列的分类来预测蛋白质的功能是分子生物研究的一个核心目标。介绍并实现了一种特征提取的方法,并将其应用于神经网络构造分类器,用来对未知类别的DNA序列分类,将分类结果与当前应用广泛的BLAST算法的结果进行比较和分析。     

基于BP神经网络的信息安全风险评估

论文详细给出了信息安全的风险评估流程及评价方法。建立的信息安全风险评估体系采用BP神经网络方法,是一种非线性方法,不带有明显的主观成分和人为因素,使评价结果更有效、更客观。实例表明计算结果贴近成功案例结果。     

Novel angular binary pattern (NABP) and kernel based convolutional neural networks classifier for cataract detection

Multimedia Tools and Applications - Cataract is considered as one of the foremost causes of blindness, especially among older people. In India, by the age 80, nearly half of older population either...     

A visual neural classifier

A new neural classifier allows visualization of the training set and decision regions, providing benefits for both the designer and the user. We demonstrate the visualization capabilities of this visual neural classifier using synthetic data, and compare the visualization performance to Kohunen's self-organizing map. We show in applications to image segmentation and medical diagnosis that visualization enables a designer to refine the classifier to achieve low error rates and enhances a user's ability to make classifier-assisted decisions.     

Output feedback control of hypersonic vehicles based on neural network and high gain observer

In this paper,the output feedback control problem for a genetic hypersonic vehicle is considered under the restriction that only the vehicle's velocity and altitude are measurable. High gain observers (HGO) are utilized to provide estimation signals for unmeasurable derivatives of the vehicle's velocity and altitude. Neural network based feedforward function is designed to compensate for model uncertainties. The proposed control design require less knowledge of the hypersonic vehicle's dynamic model. A comp...     

Improvement of neural network classifier using floating centroids

This paper presents a novel technique—Floating Centroids Method (FCM) designed to improve the performance of a conventional neural network classifier. Partition space is a space that is used to categorize data sample after sample is mapped by neural network. In the partition space, the centroid is a point, which denotes the center of a class. In a conventional neural network classifier, position of centroids and the relationship between centroids and classes are set manually. In addition, number of centroids is fixed with reference to the number of classes. The proposed approach introduces many floating centroids, which are spread throughout the partition space and obtained by using K-Means algorithm. Moreover, different classes labels are attached to these centroids automatically. A sample is predicted as a certain class if the closest centroid of its corresponding mapped point is labeled by this class. Experimental results illustrate that the proposed method has favorable performance especially with respect to the training accuracy, generalization accuracy, and average F-measures.     

Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: A review and state-of-the-art

In this paper, we proposed a multi-objective Pareto based particle swarm optimization (MOPPSO) to minimize the architectural complexity and maximize the classification accuracy of a polynomial neural network (PNN). To support this, we provide an extensive review of the literature on multi-objective particle swarm optimization and PNN. Classification using PNN can be considered as a multi-objective problem rather than as a single objective one. Measures like classification accuracy and architectural complexity used for evaluating PNN based classification can be thought of as two different conflicting criterions. Using these two metrics as the criteria of classification problem, the proposed MOPPSO technique attempts to find out a set of non-dominated solutions with less complex PNN architecture and high classification accuracy. An extensive experimental study has been carried out to compare the importance and effectiveness of the proposed method with the chosen state-of-the-art multi-objective particle swarm optimization (MOPSO) algorithm using several benchmark datasets. A comprehensive bibliography is included for further enhancement of this area.     

Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images

The performance of supervised classification algorithms is highly dependent on the quality of training data. Ambiguous training patterns may misguide the classifier leading to poor classification performance. Further, the manual exploration of class labels is an expensive and time consuming process. An automatic method is needed to identify noisy samples in the training data to improve the decision making process. This article presents a new classification technique by combining an unsupervised learning technique (i.e. fuzzy c-means clustering (FCM)) and supervised learning technique (i.e. back-propagation artificial neural network (BPANN)) to categorize benign and malignant tumors in breast ultrasound images. Unsupervised learning is employed to identify ambiguous examples in the training data. Experiments were conducted on 178 B-mode breast ultrasound images containing 88 benign and 90 malignant cases on MATLAB® software platform. A total of 457 features were extracted from ultrasound images followed by feature selection to determine the most significant features. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and Mathew's correlation coefficient (MCC) were used to access the performance of different classifiers. The result shows that the proposed approach achieves classification accuracy of 95.862% when all the 457 features were used for classification. However, the accuracy is reduced to 94.138% when only 19 most relevant features selected by multi-criterion feature selection approach were used for classification. The results were discussed in light of some recently reported studies. The empirical results suggest that eliminating doubtful training examples can improve the decision making performance of expert systems. The proposed approach show promising results and need further evaluation in other applications of expert and intelligent systems.     

基于D-S证据理论和BP神经网络的多传感器信息融合

针对多传感器信息融合的基本可信度分配在实际应用中难以解决的问题,提出了一种基于D-S证据理论与BP网络相结合的多传感器信息融合的改进方法。该方法充分发挥BP神经网络自学习、自适应和容错的能力,利用BP神经网络处理证据理论的基本可信度问题,再利用D-S证据理论来处理不精确、模糊的信息。最后通过一个实例证明了该方法的有效性。     

A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information

This paper presents a neural network approach to multiple-objective cutting parameter optimization for planning turning operations. Productivity, operation cost, and cutting quality are considered as criteria for optimizing machining operations. A feedforward neural network and a dynamic training procedure are proposed for modeling manufacturers' preferences using sampled fuzzy preferential data. Optimum cutting parameters are determined based on neural network representations of manufacturers' fuzzy preference structures.