一种基于椭圆区域的进化式模糊分类系统

介绍了一种进化式模糊分类系统.首先,介绍系统的基本特征及结构框架.然后,介绍了一种动态聚类算法,并运用动态聚类算法对输入的训练模式进行动态聚类,每一簇创建一条模糊规则.规则所对应的区域为类椭圆形区域.规则调整的策略是连续改变模糊分类规则的一个参数,使得分类系统对训练模式识别率不能再提高,对不能达到要求的调整,采用遗传算法进行调整.分析了规则调整的方法,给出了调整算法,也介绍了规则的插入和聚合策略.用两个典型的数据集来评测研究的系统,研究的分类系统在识别率与多层神经网络分类器相当,但训练时间远少于多层神经网络分类器的训练时间.     

基于混沌神经网络的分类算法

提出了一种基于混沌神经网络的分类算法,利用改进的进化策略对多个三层前馈混沌神经网络同时进行训练.训练好各个分类模型以后,将待识别数据分别输入,混沌神经网络分类模型输出最终分类结果.实验结果表明,该算法可以较好地进行数据分类,而且与传统的神经网络算法以及决策树算法相比,在分类精度和识别率方面均有一定的改善,体现出较好的稳定性.     

基于进化神经网络的短期电网负荷预测算法

本文提出了一种基于进化神经网络的短期电网负荷预测算法。该算法使用改进的人工蜂群算法与BP神经网络融合生成进化神经网络,然后使用改进的人工蜂群算法对进化神经网络的偏置和权重进行优化。该算法将火电历史负荷数据作为输入,使用进化神经网络训练预测模型,预测未来一段时间内的电网负荷。首先,获取历史负荷数据。然后,将获取到的数据输入到进化神经网络模型中进行训练。在训练过程中,采用了改进的人工蜂群算法对进化神经网络对神经网络的权重和偏置进行优化,提高模型的预测精度。人工蜂群算法作为一种全局搜索算法,可以有效地探索模型参数空间,找到最优的模型参数组合,从而提高模型的预测精度。为了验证所提出的负荷预测方法的有效性,我们使用了火电网负荷数据进行了测试。实验结果表明本文提出的进化神经网络在短期电网负荷预测方面表现出了良好的预测精度和实用性。与传统的预测方法相比,该算法的预测误差更小,预测结果更加准确可靠。     

一种基于进化神经网络的混合入侵检测模型

为了提高入侵检测系统的检测率并降低误报率,将误用检测技术和异常检测技术进行结合,以克服采用单一技术的缺陷。采用改进的进化神经网络作为检测引擎,首先,通过对遗传算法进行改进,弥补实数编码全局寻优能力差的缺陷,且降低计算的复杂度,提高进化收敛速度;然后,将改进的遗传算法和BP神经网络的LM算法进行结合,进一步克服神经网络学习阶段训练速度慢和易陷入局部最优的缺点,进而提高神经网络的分类能力和模式识别能力。采用 KDDCUP99数据集作为训练与测试数据集进行实验,结果表明,基于改进的进化神经网络建立的混合入侵检测模型在数据特征规则的提取速度、检测精度以及识别新的攻击类型方面有明显改善。     

用于航空发动机动态辨识的MSMEA-ELM算法

针对传统思维进化算法搜索半径缺乏目的性,临时子群体补充缺乏方向性以及神经网络训练速度慢、泛化能力不足,传统极端学习机隐含层神经元个数多的缺点,提出一种多群体自适应思维进化算法优化的极端学习机(MSMEA-ELM)算法,通过传感器数据训练该算法用于对航空发动机大范围动态过程进行辨识.以训练均方误差与权值2范数的加权和最小为优化目标,采用多群体自适应思维进化算法优化极端学习机.以某型涡扇发动机为研究对象,采用MSMEA-ELM算法进行航空发动机动态过程辨识,验证了该算法的有效性.     

基于进化式模糊神经网络的时间序列预测系统

文章介绍了一种基于进化式模糊神经网络时间预测系统,它是一种快速自适应的局部学习模型;进化式模糊神经网络是一个特殊类型的神经网络,它能通过进化其结构和参数来容纳新的数据.文章重点介绍了网络结构、学习方法及创建、修剪、聚合规则节点的算法;实验结果表明:模糊隶属函数的个数,规则的修剪和聚合等训练参数,与网络的行为和预测结果有很重要的关系.     

进化回归神经网络对时序数据和关联数据的建模能力研究

重点研究进化回归神经网络对时序数据和关联数据的建模能力。针对两个标准问题，采用不同形式的建模数据，比较了前向网络和回归神经网络的建模及预测效果，进一步将进化算法用于不同结构回归神经网络的训练并比较了它们的建模能力。仿真结果表明回归神经网络对时序关联数据有很好的建模和预测能力，相比于前向网络，无需过程时序特点的先验知识，可以采用最简单的建模数据形式。而进化算法相比于常规的梯度下降算法，用于训练不同的回归网络结构通用性好，且训练过程不受局部极小问题的困扰，适当规模的训练过程可以获得性能良好的神经网络模型。     

一种基于Pareto最优概念和神经网络混合策略的多目标进化算法

基于Pareto最优的多目标进化算法得到了广泛地应用,但不适用于目标函数为非解析式的情况。基于神经网络和Pareto最优的联合策略,提出了一种解决此类问题的新方法：首先采用神经网络对历史数据进行学习,建立有效的神经网络模型来代替目标函数解析式;然后将神经网络模型嵌入多目标进化算法,进行进化计算;最后,将本文方法应用于卷烟配方比例掺配问题。实验结果表明,该方法优于传统方法,能较好地解决问题。     

基于改进郊狼优化算法的浅层神经进化方法研究

神经进化作为一种不同于随机梯度下降的神经网络训练方法,现已成为机器学习研究领域的一个重要分支.如何设计更好的进化策略,探索新型神经网络权值的神经进化方法是当前研究的热点问题之一.本文提出了一种基于改进郊狼优化算法的浅层神经网络进化方法.该方法首先通过引入自适应影响权重因子与选择性的混沌扰动执行机制分别从收敛速度和寻优能力两个方面对传统郊狼优化算法进行了改进.其次,以改进郊狼优化算法为神经进化策略,融入到浅层神经网络的神经进化过程,并以BP神经网络为例,构建了一种全新的BP神经网络权值、阈值优化更新方法.最后,文中采用UCI标准数据库中几组代表性数据验证了算法的有效性.实验结果表明:改进郊狼优化算法的进化策略充分发挥了启发式优化算法在BP神经网络参数空间中的全局寻优能力,能够快速逼近最优解,经过神经进化后的BP神经网络在分类任务中表现出了优异性能,充分验证了改进郊狼优化算法作为一种新型神经进化策略的可行性和有效性.本文研究成果丰富并拓展了神经进化领域的研究内容,为构建以神经进化为主体的新型机器学习工具箱提供了重要的参考依据.     

BP网络进化及其在雷达目标识别中的应用

针对常规ＢＰ神经网络的ＢＰ算法只能训练固定结构的神经网络,存在诸如易落入局部极值、没有引入提高泛化能力的训练机制等固有不足之处,以及一些神经网络进化算法的进化机制中存在的缺陷,本文提出一种ＢＰ神经网络进化算法,并用于高分辨雷达目标一维距离像的识别问题。实验结果表明,经所述方法优化后的神经网络结构简单、泛化能力优于ＢＰ算法和一些进化算法训练的网络。     

A new ensemble feature selection and its application to pattern classification

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.     

A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery

A literature survey and analysis of the use of neural networks for the classification of remotely-sensed multi-spectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding, (2) output encoding and extraction of classes, (3) network architecture, (4) training algorithms, and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its nonparametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.     

Boosting a fast neural network for supervised land cover classification

It is demonstrated that the use of an ensemble of neural networks for routine land cover classification of multispectral satellite data can lead to a significant improvement in classification accuracy. Specifically, the AdaBoost.M1 algorithm is applied to a sequence of three-layer, feed-forward neural networks. In order to overcome the drawback of long training time for each network in the ensemble, the networks are trained with an efficient Kalman filter algorithm. On the basis of statistical hypothesis tests, classification performance on multispectral imagery is compared with that of maximum likelihood and support vector machine classifiers. Good generalization accuracies are obtained with computation times of the order of 1 h or less. The algorithms involved are described in detail and a software implementation in the ENVI/IDL image analysis environment is provided.     

Selecting salient features for classification based on neural network committees

Aggregating outputs of multiple classifiers into a committee decision is one of the most important techniques for improving classification accuracy. The issue of selecting an optimal subset of relevant features plays also an important role in successful design of a pattern recognition system. In this paper, we present a neural network based approach for identifying salient features for classification in neural network committees. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. The accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features.     

Restricted Bayesian classification networks

Bayesian networks are graphical models that describe dependency relationships between variables, and are powerful tools for studying probability classifiers. At present, the causal Bayesian network learning method is used in constructing Bayesian network classifiers while the contribution of attribute to class is over-looked. In this paper, a Bayesian network specifically for classification-restricted Bayesian classification networks is proposed. Combining dependency analysis between variables, classification accuracy evaluation criteria and a search algorithm, a learning method for restricted Bayesian classification networks is presented. Experiments and analysis are done using data sets from UCI machine learning repository. The results show that the restricted Bayesian classification network is more accurate than other well-known classifiers.     

基于多进化神经网络的信用评估模型研究

客户信用评佑对于银行的经营管理有着重要的意义,为此提出了一种基于多进化神经网络的信用评估模型(MNN-CREDIT)。该模型基于客户信货数据,利用基于聚类的小生境遗传算法并行地训练出多个精度高、差异性大的三层前馈神经网络,然后将待识别的客户数据分别输入,最后根据动态投票法集成最终信用预测结果。利用德国信用数据库真实数据集进行了实证分析,结果表明,基于多进化神经网络的信用评估模型具有较高的预测精度。     

Rules extraction from constructively trained neural networks based on genetic algorithms

The application of neural networks in the data mining has become wider. Although neural networks may have complex structure, long training time, and the representation of results is not comprehensible, neural networks have high acceptance ability for noisy data, high accuracy and are preferable in data mining. On the other hand, It is an open question as to what is the best way to train and extract symbolic rules from trained neural networks in domains like classification. In this paper, we train the neural networks by constructive learning and present the analysis of the convergence rate of the error in a neural network with and without threshold which have been learnt by a constructive method to obtain the simple structure of the network.The response of ANN is acquired but its result is not in understandable form or in a black box form. It is frequently desirable to use the model backwards and identify sets of input variable which results in a desired output value. The large numbers of variables and nonlinear nature of many materials models that can help finding an optimal set of difficult input variables. We will use a genetic algorithm to solve this problem. The method is evaluated on different public-domain data sets with the aim of testing the predictive ability of the method and compared with standard classifiers, results showed comparatively high accuracy.     

基于k-means聚类的神经网络分类器集成方法研究

针对差异性是集成学习的必要条件,研究了基于k-means聚类技术提高神经网络分类器集成差异性的方法。通过训练集并使用神经网络分类器学习算法训练许多分类器模型,在验证集中利用每个分类器的分类结果作为聚类的数据对象;然后应用k-means聚类方法对这些数据聚类,在聚类结果的每个簇中选择一个分类器代表模型,以此构成集成学习的成员;最后应用投票方法实验研究了这种提高集成学习差异性方法的性能,并与常用的集成学习方法bagging、adaboost进行了比较。     

Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.