基于混合染色体的带钢缺陷图像分类方法研究

为了保证带钢缺陷分类的实时性和准确性,提出了一种基于混合染色体的带钢缺陷图像分类方法。该方法不仅优化了支持向量机SVM中核函数参数、惩罚因子,并且对核函数、输入的特征向量进行了选择。除此之外,该方法融合了遗传算法和SVM,用遗传算法优化影响SVM的核函数参数、惩罚因子、输入特征和核函数;同时,用SVM建立的分类模型的分类准确率限制遗传算法的进化方向,彼此制约和促进,最终确定最优分类模型。实验结果表明,基于混合染色体的带钢缺陷图像分类方法建立的分类模型能实时、准确地对带钢缺陷图像进行分类。     

惩罚因子在重复囚徒困境博弈中的研究

针对囚徒困境博弈中收益矩阵参数无法动态更新的问题,提出一个带惩罚因子的囚徒困境博弈模型。该模型中的个体可以根据自身的策略,动态修改收益矩阵中的参数,在每轮博弈之后个体根据邻居收益更新自己的策略。仿真结果表明,惩罚因子可以有效地促进合作策略的涌现,另外发现,合作策略的涌现不仅与惩罚因子有关,而且与网络个体的初始策略有关。当社团内部的个体采取相同策略,社团之间采取不同策略时,更有利于合作策略的涌现。最后,惩罚因子还可以提高参与者的平均收益。     

基于子编码和全编码联合惩罚的稀疏表示字典学习方法

针对目前稀疏表示字典学习的惩罚函数版本不一且各有优势的问题,提出基于子编码和全编码联合惩罚的稀疏表示字典学习方法,该方法在字典学习的目标函数中同时加入子编码惩罚函数和全编码惩罚函数。子编码惩罚函数使得学习后的字典在稀疏表示识别时可以用子字典的重构误差和子字典上编码系数的大小来识别,全编码惩罚函数则能直接利用整个字典上的编码系数来识别,通过联合这两个惩罚函数可以获得非常好的识别效果。为了验证所提方法的有效性,在语音情感库和人脸库上与最新的基于字典学习的稀疏表示识别方法 DKSVD和FDDL进行对比,并与著名的识别方法SVM和SRC进行比较,实验结果显示所提方法具有更好的识别性能。     

多节点流水线生产订单接受与调度一体化问题

为了使企业在选择订单时获取最大利润,避免产生惩罚损失,提出了一种流水线生产企业订单接受与调度一体化的决策方法。在该方法中,将订单接受与调度同时规划,建立了以利润最大化为目的,考虑拖期惩罚的决策模型。提出一种新的基于模拟退火过程的启发式求解算法来求解该模型,实例验证了算法的有效性。     

多通道并行传输中基于收益最大化的流量分配方法

针对已有的多通道并行传输中的流量分配方法不区分业务、也未考虑资源收益的问题,分析了业务参数模型,引入业务的收益作为多通道并行传输中流量分配的效益评价函数,通过将流量分配映射为带宽分配建立了基于收益最大化的多通道并行传输流量分配模型。基于边际收益递减法则,提出了一种边际收益优先的多业务流多通道并行传输流量分配算法,该算法先根据边际收益优先的贪心策略获取最大化收益的流量分配方案,然后通过调整业务在不同通道间的流量来保证业务的最低质量要求,最后通过典型业务实例分析和仿真实验验证了算法的有效性。与基于带宽比例策略的流量分配算法相比,所提出的方法能够在保证业务质量的前提下,实现带宽资源的优化配置,提高运营商的收益。     

改进SVM不平衡数据分类的IGWOSMOTE方法

为了改善传统支持向量机SVM对不平衡数据集中少数类的分类效果,提出一种基于改进灰狼算法(IGWO)的过采样方法——IGWOSMOTE。首先,改进初始灰狼种群的生成形式,由SVM的惩罚因子、核参数、特征向量和少数类的采样率组成灰狼个体;然后,经由灰狼优化过程智能搜索获得最优相关参数和最优采样率组合,进行重新采样供分类器学习及预测。通过对6个UCI数据集的分类实验得出：IGWOSMOTE+SVM较传统SMOTE+SVM方法在少数类分类精度上提高了6.3个百分点,在整体数据集分类精度上提高了2.1个百分点,IGWOSMOTE可作为一种新的过采样分类方法。     

基于主成分分析的自适应特征选择算法研究

提出了一种自适应性的特征提取方法。首先通过主成分分析求出样本全局投影空 间,然后基于最大化投影构建优化目标函数,最后通过该函数求出自适应于个体样本的投影空 间。该方法很好地考虑了样本集合中每个样本的分布特点。为了使得算法可应用于识别分类问 题中,给出了计算存在于不同投影空间的个体样本间相似性的方法,相比于欧式度量,该方法 被证明得到的相似性能够更好地表征样本间的测地距离关系,使其能够有效地对流型结构数据 进行学习。通过在不同数据库上进行分类及重构的对比实验,实验结果表明,该方法能够更好 地提取数据特征,且对离群点具有鲁棒性。     

基于遗传算法的最优直方图阈值图像分割算法

为了保证遗传算法能够尽快收敛到全局最优解,避免早熟现象发生,提出了适应度标定公式,保证适应度函数值总为正值。新的适应度函数能够正确引导群体的发展方向,提高选择压力;提出了相似度概念,保留相似性差的个体,剔除相似性个体。在不增加群体规模的前提下,增加了群体的多样性。为了有效地对图像进行分割,提出基于改进遗传算法的图像分割方法,采用Otsu公式,找出分割图像最优阈值。给出不同改进遗传算法计算实例比较和不同图像分割方法效果图。     

无线传感器网络路由中合作性重复博弈模型的研究

无线传感器网络中,节点能耗、路径可靠度以及节点的死亡时间是传感器网络路由需要考虑的关键因素.为了提高能量利用率和传感器网络收益,在节点理性且自私的条件下,运用博弈论方法提出了一种基于节点合作的数据包发送/转发的重复博弈模型,设计了一个与路径连通度和节点能量消耗有关的收益函数,采用惩罚机制使重复博弈模型存在子博弈精炼纳什均衡,降低了自私节点背叛的可能性.实验结果表明:采用惩罚机制的重复博弈能够提高网络的收益,同时也提高了网络吞吐量,任何自私节点的不合作行为都导致节点的能量浪费和节点的整体收益下降.     

融合渐近性的灰狼优化支持向量机模型

大数据的发展对数据分类领域的分类准确性有了更高的要求;支持向量机(Support Vector Machine,SVM)的广泛应用需要一种高效的方法来构造一个分类能力强的SVM分类器;SVM的核函数参数与惩罚因子以及特征子集对预测模型的复杂度和预测精度有着重要影响。为提高SVM的分类性能,文中将SVM的渐近性融合到灰狼优化(Grey Wolf Optimization,GWO)算法中,提出了新的SVM分类器模型,该模型对SVM的参数与数据的特征子集同时进行优化,融合SVM渐近性的新灰狼个体将灰狼优化算法的搜索空间导向超参数空间中的最佳区域,能够更快地获得最优解;此外,将获得的分类准确率、所选特征个数和支持向量个数相结合,提出了一种新的适应度函数,新的适应度函数与融合渐近性的灰狼优化算法将搜索引向最优解。采用UCI中的多个经典数据集对所提模型进行验证,将其与网格搜素算法、未融合渐近性的灰狼优化算法以及其他文献中的方法进行对比,其分类准确率在不同数据集上均有不同程度的提升。实验结果表明,所提算法能找到SVM的最优参数与最小特征子集,具有更高的分类准确率和更短的平均处理时间。     

Benchmarks for scheduling on a single machine against restrictive and unrestrictive common due dates

We consider the NP-hard problem of scheduling jobs on a single machine against common due dates with respect to earliness and tardiness penalties. The paper covers two aspects: Firstly, we develop a problem generator and solve 280 instances with two new heuristics to obtain upper bounds on the optimal objective function value. Secondly, we demonstrate computationally that our heuristics are efficient in obtaining near-optimal solutions for small problem instances. The generated problem instances in combination with the upper bounds can be used as benchmarks for future approaches in the field of common due-date scheduling.Scope and purposeIn connection with just-in-time production and delivery, earliness as well as tardiness penalties are of interest. Thus scheduling against common due dates has received growing attention during the last decade. Many algorithms have been developed to solve the different variants of this problem. But whenever a new algorithm for scheduling against common due dates is proposed, its quality is assessed only on a few self-generated examples. Hence it is difficult to evaluate the various approaches, particularly in comparison with each other. Therefore the goal of this paper is to present numerous benchmark problems together with some upper bounds on the optimal objective function value.     

Artificial neural networks with evolutionary instance selection for financial forecasting

In this paper, I propose a genetic algorithm (GA) approach to instance selection in artificial neural networks (ANNs) for financial data mining. ANN has preeminent learning ability, but often exhibit inconsistent and unpredictable performance for noisy data. In addition, it may not be possible to train ANN or the training task cannot be effectively carried out without data reduction when the amount of data is so large. In this paper, the GA optimizes simultaneously the connection weights between layers and a selection task for relevant instances. The globally evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, genetically selected instances shorten the learning time and enhance prediction performance. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in ANN.     

面向聚类分析的自适应弹性网络算法研究

弹性网络算法是一种启发式算法,最初被提出是用来解决TSP（Traveling Salesman Problem）问题的,现如今,被广泛应用于聚类问题中,尤其对于高维空间数据聚类方面,有很大的优势。提出了一种新的自适应弹性网络算法（Adaptive Elastic Net,AEN）解决聚类问题,该算法利用弹性网络算法得到的[K]个中心点作为聚类初始中心点,并利用局部搜索择优算法在每次迭代中更新中心点。以聚类完成后每一簇的中心点到该簇元素的距离之和作为聚类质量评价标准,分别对随机生成的不同维度的50,100,300,500,1?000个数据点的数据集和UCI中多个标准数据集进行聚类,并将结果与传统聚类算法的聚类结果进行比较。实验表明：相较于传统的聚类算法,该算法可以有效地提高聚类质量。     

A lattice model for cellular (systolic) algorithms

In this paper a lattice model (here ‘lattice’ means a net of points) is introduced for homogeneous cellular algorithms. On the basis of this model a transformation methodology is developed, which makes it possible to produce many different versions of a given cellular algorithm. These versions may have quite different structural properties, but they perform the same computation as the original algorithm. In this way a great variety of cellular algorithms can be offered to choose the best version in practice and, on the other hand, cellular algorithms can be classified according to their inherent structures.     

基于决策树的神经网络

传统人工神经网络模型采用试探的方法确定合适的网络结构,并随机地初始化参数值,导致神经网络训练效率低、结果不稳定.熵网络是一种建立在决策树之上的3层前馈网络,在熵网络基础上,提出了基于决策树的神经网络设计方法(DTBNN).DTBNN中提供了对神经网络参数的初始值合理设置的方法,并提出了由决策树确定的只是熵网络的初始结构,在实际的网络构造中需要根据实际应用添加神经元和连接权以提高网络的性能.理论分析和实验结果表明了这种方法的合理性.     

Approximating support vector machine with artificial neural network for fast prediction

Support vector machine (SVM) is a powerful algorithm for classification and regression problems and is widely applied to real-world applications. However, its high computational load in the test phase makes it difficult to use in practice. In this paper, we propose hybrid neural network (HNN), a method to accelerate an SVM in the test phase by approximating the SVM. The proposed method approximates the SVM using an artificial neural network (ANN). The resulting regression function of the ANN replaces the decision function or the regression function of the SVM. Since the prediction of the ANN requires significantly less computation than that of the SVM, the proposed method yields faster test speed. The proposed method is evaluated by experiments on real-world benchmark datasets. Experimental results show that the proposed method successfully accelerates SVM in the test phase with little or no prediction loss.     

A new architecture selection method based on tabu search for artificial neural networks

Artificial neural networks (ANN) have been used in various applications in recent years. One of these applications is time series forecasting. Although ANN produces accurate forecasts in many time series implementations, there are still some problems with using ANN. ANN consist of some components such as architecture structure, learning algorithm and activation function. These components have important effect on the performance of ANN. An important decision is the selection of architecture structure that consists of determining the numbers of neurons in the layers of a network. Therefore, various approaches have been proposed to determine the best ANN architecture in the literature. However, the most preferred method is still trial and error method for finding a good architecture. In this study, a new architecture selection method based on tabu search algorithm is proposed. In the implementation, five real time series are analyzed by using ANN and the proposed method is employed to select the best architecture. For the comparison, these time series are also forecasted by using ANN when trial and error method is utilized to determine the best architecture. As a result of the implementation, it is clearly seen that better results are obtained when the proposed method is used for the selection of architecture.     

In-depth analysis and simulation study of an innovative fuzzy approach for ranking alternatives in multiple attribute decision making problems based on TOPSIS

In this paper, an innovative fuzzy approach for ranking alternatives in multiple attribute decision making problems based on TOPSIS is presented in-depth and studied through simulation comparison with the original method. The TOPSIS method provides the principle of compromise that the chosen alternative should have the shortest distance from the ideal solution and, simultaneously, the farthest distance from the negative ideal solution. However, the TOPSIS method does not always produce results in harmony with this principle due to an oversimplified definition of its aggregation function which does not grasp the contradictory nature of the principle's formulation. Our approach addresses this issue through the introduction of a fuzzy set representation of the closeness to the ideal and to the negative ideal solution for the definition of the aggregation function which is modeled as the membership function of the intersection of two fuzzy sets. This model enables a parameterization of the method according to the risk attitude of the decision maker. Thus, a class of methods is formulated whose different instances correspond to different risk attitudes of the decision makers. In order to define some clear advises for decision makers facilitating a proper parameterization of the method, a comparative analysis of the proposed class of methods with the original TOPSIS method is performed according to well defined simulation techniques. The results of the simulation experiment show on the one hand that there is no direct correspondence between the proposed class of methods and TOPSIS, and on the other hand that it is adequate to distinguish three instances that correspond respectively to risk-averse, risk-neutral and risk-seeking decision makers. Finally, a numerical example pertaining to the problem of service provider selection is presented to illustrate the application of the proposed class of methods and its functioning.     

Semi-supervised learning combining co-training with active learning

Co-training is a good paradigm of semi-supervised, which requires the data set to be described by two views of features. There are a notable characteristic shared by many co-training algorithm: the selected unlabeled instances should be predicted with high confidence, since a high confidence score usually implies that the corresponding prediction is correct. Unfortunately, it is not always able to improve the classification performance with these high confidence unlabeled instances. In this paper, a new semi-supervised learning algorithm was proposed combining the benefits of both co-training and active learning. The algorithm applies co-training to select the most reliable instances according to the two criterions of high confidence and nearest neighbor for boosting the classifier, also exploit the most informative instances with human annotation for improve the classification performance. Experiments on several UCI data sets and natural language processing task, which demonstrate our method achieves more significant improvement for sacrificing the same amount of human effort.     

Optimization of neural network model using modified bat-inspired algorithm

The success of an artificial neural network (ANN) strongly depends on the variety of the connection weights and the network structure. Among many methods used in the literature to accurately select the network weights or structure in isolate; a few researchers have attempted to select both the weights and structure of ANN automatically by using metaheuristic algorithms. This paper proposes modified bat algorithm with a new solution representation for both optimizing the weights and structure of ANNs. The algorithm, which is based on the echolocation behaviour of bats, combines the advantages of population-based and local search algorithms. In this work, ability of the basic bat algorithm and some modified versions which are based on the consideration of the personal best solution in the velocity adjustment, the mean of personal best and global best solutions through velocity adjustment and the employment of three chaotic maps are investigated. These modifications are aimed to improve the exploration and exploitation capability of bat algorithm. Different versions of the proposed bat algorithm are incorporated to handle the selection of the structure as well as weights and biases of the ANN during the training process. We then use the Taguchi method to tune the parameters of the algorithm that demonstrates the best ability compared to the other versions. Six classifications and two time series benchmark datasets are used to test the performance of the proposed approach in terms of classification and prediction accuracy. Statistical tests demonstrate that the proposed method generates some of the best results in comparison with the latest methods in the literature. Finally, our best method is applied to a real-world problem, namely to predict the future values of rainfall data and the results show satisfactory of the method.