粗糙自组织映射在基因表达数据分析中的应用

本文利用粗糙集与布尔逻辑离散约简算法改进了粗糙自组织映射算法,并应用于基因表达数据的分析中.算法改进了传统自组织映射收敛慢、网络规模难以确定的缺点,减小了网络规模不确定对分类效果的影响.使用酵母茵基因表达数据进行实验,得到了较好的网络质量、网络规模和分类效果,相比传统自组织映射使分类正确率提高了10.15%.     

基于改进的自组织映射网络的遥感影像融合研究

多源遥感影像融合是富集遥感海量数据的最有价值的技术手段。本文给出了一种新的基于改进的自组织映射网络的遥感影像融合模型。选择浙江绍兴为典型研究区，以Landsat TM(10m)与SPOT-4 Pan(10m)融合数据为例，进行了融合实验与分析。实验结果表明，应用基于改进的自组织映射网络模型进行融合，分类融合结果较好，较基于基本自组织映射网络的影像融合分类精度提高约8％。     

基于自组织特征映射网络的气体识别方法研究

针对气体传感器的交叉敏特性,提出将气体传感器阵列与模式识别上结合形成人工嗅觉系统,用于气体的定性识别和定量检测。利用６个半导体传感器组成阵列,并结合自组织特征映射网络模式识别技术准确地识别了ＣＯ、Ｈ２、ＣＨ４等三种气体,说明人工嗅觉系统能够提高气体伟感器的选择性,自组织牧映射网络应用于气体识别是可行的。     

一种高效的自组织特征映射图的初始化方法

自组织特征映射图算法(SOFM,self-organizing Feature Map)在模式识别中有着广泛的应用.本文首先讨论了网络结构的初始化设置对自组织特征映射图构造的影响以及加速SOFM网络学习训练过程的主要方法,然后提出一种从边界到中心的自组织特征映射图初始化方法,该方法形成的自组织特征映射图能够真实地表示输入样本内在关系,大大减少学习训练次数,从而有效改进了传统的SOFM算法.     

改进的基于SOM的高维数据可视化算法

通过对高维数据可视化方法的系统研究,提出了一种新的基于自组织映射（Self-Organizing Map,SOM）的算法。为了表现该方法的特点,将其称为三维自组织映射（Three-Dimensional SOM,TDSOM）。它在对高维数据记录集进行SOM分析后将其投影到三维坐标系中的特定的点集上,最终形成三维模型。该模型弥补了传统模型难以清晰准确地展现高维数据的缺陷,并且新模型着重于在一个比二维平面更为广阔的三维立体空间中展现海量数据。使用者通常可以根据当前领域的专业知识在分析模型的基础上得出有意义的模式。新方法可以广泛使用在数据挖掘和模式识别等领域。     

基于GHSOM网络的时间序列聚类方法

提出了一种基于增长型分层自组织映射(GHSOM)的时间序列聚类方法，给出了该方法的基本原理和具体算法步骤，对实测时间序列数据进行了聚类验证和分析。研究结果表明，增长型分层自组织映射能根据对象特征无监督地对时间序列进行正确聚类，由于具有动态增长及分层特性，能分析对象内在的层次结构并实现由粗到精的聚类，可以扩展应用于大型乃至巨量时间序列数据库的模式发现。     

自组织映射（S0M）型神经网络的实现

文章讨论了应用于自组织映射型神经网络的训练算法，该算法通过计算获胜神经元来找到最接近输入模式的节点．然后通过对网络连接权值的自组织，训练一个自组织竞争的神经网络，该网络通过一组输入数据可自行训练形成一个神经网络模型，     

自组织映射网络及其在化学化工领域中的应用进展

讨论了自组织映射网络（Self-organizing map,SOM）的发展与应用进展情况。首先介绍了自组织映射网络的产生、发展概况,网络的原理、基本结构和算法,网络的应用现状以及最新的研究进展情况。基本的自组织映射算法在实际应用中存在着一定的缺陷,对此着重介绍了近年来自组织映射网络的各种改进方案,以及目前在化学化工领域中的应用情况。最后提出了自组织映射网络研究中仍然存在的问题,并对其在化学化工领域中的应用发展前景做了展望。     

基于自组织映射的流形学习与可视化

针对自组织映射(SOM)在学习和可视化高维数据内在的低维流形结构时容易产生“拓扑缺陷”的这一问题,提出了一种新的流形学习算法--动态自组织映射(DSOM)。该算法按照数据的邻域结构逐步扩展训练数据集合,对网络进行渐进训练,以避免局部极值,克服“拓扑缺陷”问题;同时,网络规模也随之动态扩展,以降低算法的时间复杂度。实验表明,该算法能更加真实地学习和可视化高维数据内在的低维流形结构;此外,与传统的流形学习算法相比,该算法对邻域大小和噪声也更加鲁棒。所提算法的网络规模和训练数据集合都将按照数据内在的邻域结构进行同步扩展,从而能更加简洁并真实地学习和可视化高维数据内在的低维流形结构。     

自组织竞争网络在测井资料岩性识别中的应用

利用自组织神经网络自组织自学习的学习能力的特点,在分析影响岩性识别因子的基础上,建立了基于MATLAB的自组织竞争网络模型.通过比较基本竞争型网络和自组织特征映射网络两种网络结构实现岩性分类.实验结果表明,利用自组织竞争网络模型对测井资料岩性识别是可行的,正确率高,为岩性识别的研究提供了新方法.     

NBSOM: The naive Bayes self-organizing map

The naive Bayes model has proven to be a simple yet effective model, which is very popular for pattern recognition applications such as data classification and clustering. This paper explores the possibility of using this model for multidimensional data visualization. To achieve this, a new learning algorithm called naive Bayes self-organizing map (NBSOM) is proposed to enable the naive Bayes model to perform topographic mappings. The training is carried out by means of an online expectation maximization algorithm with a self-organizing principle. The proposed method is compared with principal component analysis, self-organizing maps, and generative topographic mapping on two benchmark data sets and a real-world image processing application. Overall, the results show the effectiveness of NBSOM for multidimensional data visualization.     

Associative learning method in a hypercolumn model

We propose an associatively learnable hypercolumn model (AHCM). A hyper-column model is a self-organized, competitive, and hierarchical multilayer neural network. It is derived from the neocognitron by replacing each S cell and C cell with a two-layer hierarchical self-organizing map. The HCM can recognize images with variant object size, position, orientation and spatial resolution. However, feature maps may integrate some features extracted in the lower layer even if the features are extracted from input data which belong to different categories. The learning algorithm of the HCM causes this problem because it is an unsupervised learning used by a self-organizing map. An associative learning method is therefore introduced, which is derived from the HCM by appending associative signals and associative weights to traditional input data and connection weights, respectively. The AHCM was applied to hand-shape recognition. We found that the AHCM could generate an appropriate feature map and higher recognition accuracy compared with the HCM. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Color image segmentation using parallel OptiMUSIG activation function

Segmentation of the different feature based data in a dataset is a challenging proposition in the image processing community. There exist different techniques to solve this problem satisfactorily. A color image is an example of three-dimensional dataset and it consists of a collection of three primary color intensity features. In this article, we focus on the segmentation of true color test images, based on all possible combination of color intensity features. A multilevel sigmoidal (MUSIG) activation function that is applied in the self-organizing neural network architecture is quite efficient enough to segment multilevel gray level intensity images. The function uses equal and fixed class responses, ignoring the heterogeneity of image information content. The optimized version of MUSIG (OptiMUSIG) activation function for the self-organizing neural network architecture can be generated with the optimized class responses from the image content and can be used to effectively segment multilevel gray level intensity images as well.     

Improvement of early recognition of gesture patterns based on a self-organizing map

We propose an approach to achieving early recognition of gesture patterns. Early recognition is a method for recognizing sequential patterns at their earliest stage. Therefore, in the case of gesture recognition, we can get a recognition result for human gestures before the gestures are finished. The most difficult problem in early recognition is knowing when the system has determined the result. Most traditional approaches suffer from this problem, since gestures are often ambiguous. At the start of a gesture, in particular, it is very difficult to determinate the recognition result since insufficient input data have been observed. Therefore, we have improved on the traditional approach by using a self-organizing map.     

用于特征维数不等识别问题的自组织模糊神经网络

一种用于特征维数不等识别问题的自组织模糊神经网络被提出。网络专为特征维数不相等一类模式识别问题而构造,具有结构自组织,基于知识和学习能力等基本特征,采用两阶段工作模式——结构学习和参数学习。网络首先根据样本自组织学习网络结构,而后再进行集中批学习网络参数。作者用基于中药材色谱保留时间和保留峰面积的特征样本进行构造、训练学习后得到了一个中药材、中成药组成药材模糊识别网络。该网络既可用于中药材的识别,也可用于识别组成中成药各味中药材的复杂模式识别问题。经实验测试,达到了预期效果,为中成药药方解析和质量控制探索出一条新路。     

A neuro-fuzzy system for chemical agent detection

The authors previously introduced a fuzzy version of Kohonen's well-known self-organizing map neural network model. In this novel neuro-fuzzy system, the neurons of Kohonen's original model are replaced by fuzzy rules. Each fuzzy rule is composed of fuzzy sets and an output singleton. Since the fuzzy self-organizing map is a modified version of Kohonen's original model, the self-organizing map and the learning vector quantization learning laws can be used to tune the neuro-fuzzy system. Originally, the fuzzy self-organizing map was intended to be used as an unknown function approximator, while Kohonen's self-organizing map is primarily used as a neural classifier. In this paper, the authors show how the fuzzy self-organizing map can also be used as a neuro-fuzzy classifier. Simulation results show that, in chemical agent detection, the fuzzy self-organizing map not only gives better classification results than Kohonen's model, but it also has smaller number of fuzzy rules than the corresponding neurons required by Kohonen's self-organizing map     

Condition monitoring of the cutting process using a self-organizing spiking neural network map

This paper presents a new approach to sensor based condition monitoring using a self-organizing spiking neuron network map. Experimental evidence suggests that biological neural networks, which communicate through spikes, use the timing of these spikes to encode and compute information in a more efficient way. The paper introduces the basis of a simplified version of the Self-Organizing neural architecture based on Spiking Neurons. The fundamental steps for the development of this computational model are presented as well as some experimental evidence of its performance. It is shown that this computational architecture has a greater potential to unveil embedded information in tool wear monitoring data sets and that faster learning occurs if compared to traditional sigmoidal neural networks.     

Topology constraint free fuzzy gated neural networks for patternrecognition

A novel topology constraint free neural network architecture using a generalized fuzzy gated neuron model is presented for a pattern recognition task. The main feature is that the network does not require weight adaptation at its input and the weights are initialized directly from the training pattern set. The elimination of the need for iterative weight adaptation schemes facilitates quick network set up times which make the fuzzy gated neural networks very attractive. The performance of the proposed network is found to be functionally equivalent to spatio-temporal feature maps under a mild technical condition. The classification performance of the fuzzy gated neural network is demonstrated on a 12-class synthetic three dimensional (3-D) object data set, real-world eight-class texture data set, and real-world 12 class 3-D object data set. The performance results are compared with the classification accuracies obtained from a spatio-temporal feature map, an adaptive subspace self-organizing map, multilayer feedforward neural networks, radial basis function neural networks, and linear discriminant analysis. Despite the network's ability to accurately classify seen data and adequately generalize validation data, its performance is found to be sensitive to noise perturbations due to fine fragmentation of the feature space. This paper also provides partial solutions to the above robustness issue by proposing certain improvements to various modules of the proposed fuzzy gated neural network.