Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

小型机载数字视频记录系统的设计与实现

采用嵌入式控制模块SOM2386和专用视频处理芯片SZ1510,设计和实现了一种基于电子硬盘存储的嵌入式小型机载数字视频记录系统。给出了系统总体方案,阐述了视音频解码编码、接口、控制及存储单元的硬件设计,介绍了系统的软件设计,并给出了系统软件的整体工作流程。该系统体积小、重量轻、抗震性好、使用维护方便,已在某型飞机上得到应用。     

深亚微米条件下的多层VLSI／MCM层分配的遗传算法

本文提出了一个深亚微米条件下的多层ＶＬＳＭＣＭ有约束分层层分配的遗传算法。该算法分为两步：首先进行超层分配，使各线网满足Ｃｒｏｓｓｔａｌｋ约束，且超层数目最少；然后进行各超层的通孔最少化二分层。与目前的层分配算法相比，该遗传算法具有目标全面，全局优化能力强等特点，是一种可应用于深亚微米条件下的ＩＣ ＣＡＤ的有效分层方法。     

声光器件的换能器带宽—换能器增透膜系的选择

通过计算机编程运算ＴＬ－Ｆ曲线来讨论换能器的带宽，实现了声增透膜系的选择。     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

基于SOM的模糊聚类在Web日志挖掘中的应用

将自组织映射神经网络（SOM）与FCM结合,利用SOM的并行计算能够减少模糊C均值算法在处理海量数据时的聚类时间,可以提高聚类算法的速度和效果,同时使用该算法对校园网Web日志进行数据挖掘,能够对用户行为进行分析,从而提出相应的方法,更好地提高服务效率和管理质量。     

基于SOM和 PCA的闽江流域地表水水质综合评价

根据2014年1月至2015年11月闽江流域19个断面的水质月均值监测数据,采用自组织特征映射网络(SOM)和主成分分析(PCA)法研究了闽江流域水质时空变化特征,并用水质指数对闽江水质进行了综合评价。SOM分析将水质样本分为3个空间群组,其中水质变化周期分2个阶段:4—11月、12月至次年3月。PCA法分析表明,春冬季沙溪和富屯溪支流以及闽江下游福州城区河段营养盐水平偏高,在春夏季上游部分河段和下游闽江口有机污染水平偏高。水质指数评价结果显示,闽江流域整体水质较好,其中三大子流域及闽江下游水质评价从优至劣顺序为:富屯溪流域、建溪流域、沙溪流域、闽江下游流域。     

基于SOM网络以线段为基元的曲线重建

在逆向工程中,对基于散乱数据点的曲线重建研究有着重要的意义。曲线可用线段基元逼近。提出使用SOM神经网络以线段为基元的曲线重建算法。给定某一曲线的散乱点集和一初始折线,使用该算法优化折线上的顶点位置,使折线更好地逼近散乱点;实验结果表明,使用该算法能够取得良好的曲线重建效果。     

基于功效特征的专利聚类方法

当前专利是按照领域划分的,而基于功效特征可以实现跨领域的专利聚类,这在企业创新设计中具有重要意义,而精确提取专利功效特征和快速获得最优聚类结果是其中的关键任务。为此提出一种信息实体语义增强表示（ERNIE）和卷积神经网络（CNN）相结合的功效特征联合提取（FEI-Joint）模型来提取专利文献的功效特征,并且改进自组织神经网络（SOM）算法,从而提出具有早期拒绝策略与类合并思想的自组织神经网络（ERCM-SOM）来实现基于功效特征的专利聚类。对FEI-Joint模型与TF-IDF、狄利克雷分布（LDA）、CNN在特征提取后的聚类效果上进行比较和分析,结果表明其F-measure值比其他模型有明显提高。ERCM-SOM算法与K-Means算法、SOM算法相比,在F-measure值提高的同时,其时间较SOM算法有明显缩短。对比使用专利分类号（IPC）的专利分类,采用基于功效特征的聚类方法可实现跨领域的专利聚类效果,为设计者借鉴其他领域的设计方法奠定了基础。