基于BP神经网络的手写体数字的识别方法研究

对手写体数字的识别问题进行了讨论,提出一种基于BP神经网络的识别方法.从而提高了识别效率.主要就在识别时,数字在图片上的位置和数字本身大小方面做了改进,发现数字在图片上的大小和其在图片上的位置直接影响识别效果.具体做的是,首先提取了图片的轮廓,然后归一化成28×28的图像.这样做,不仅使得图像数字区域大小相同,而且都在图像中心上,使得识别结果变的更加理想化,达到了高识别的目的.另外,选择了容错性较好的BP网络,以200组手写体数字图像作为输入向量,以其他的110组进行识别,效率达到了90%.     

粒子群优化模糊神经网络在语音识别中的应用

针对模糊神经网络训练采用BP算法比较依赖于网络的初始条件,训练时间较长,容易陷入局部极值的缺点,利用粒子群优化算法(PSO)的全局搜索性能,将PSO用于模糊神经网络的训练过程.由于基本PSO算法存在一定的早熟收敛问题,引入一种自适应动态改变惯性因子的PSO算法,使算法具有较强的全局搜索能力.将此算法训练的模糊神经网络应用于语音识别中,结果表明,与BP算法相比,粒子群优化的模糊神经网络具有较高的收敛速度和识别率.     

A Learning Framework for Neural Networks Using Constrained Optimization Methods

Conventional supervised learning in neural networks is carried out by performing unconstrained minimization of a suitably defined cost function. This approach has certain drawbacks, which can be overcome by incorporating additional knowledge in the training formalism. In this paper, two types of such additional knowledge are examined: Network specific knowledge (associated with the neural network irrespectively of the problem whose solution is sought) or problem specific knowledge (which helps to solve a specific learning task). A constrained optimization framework is introduced for incorporating these types of knowledge into the learning formalism. We present three examples of improvement in the learning behaviour of neural networks using additional knowledge in the context of our constrained optimization framework. The two network specific examples are designed to improve convergence and learning speed in the broad class of feedforward networks, while the third problem specific example is related to the efficient factorization of 2-D polynomials using suitably constructed sigma-pi networks.     

多层神经网络的一个快速算法

本文对文[4]提出的前馈式多层神经网络的单参数动态搜索（SPDS）算法进行了深入的分析，给出了实现快速一维搜索的两个方案，从而实现了多层神经网络更为快速的学习训练．     

模糊神经网络的一种混合递推学习算法

提出一种新型混合递推学习算法,记为FNRPCL 及FNIGLS,这种算法用来调整模糊神经网络中隶属函数的中心和宽度,以及输出层的连接权值     

遗传算法结合神经网络在油气产量预测中的应用

基于遗传算法的全局搜索能力和BP算法的局部精确搜索特性,通过采用遗传算法优化神经网络的方法,将遗传算法和BP算法有机结合,做到优势互补,在提高油气产量预测精度的研究中得到了很好的应用.在对国内某中小型气田油气产量的预测中,以历史产量资料进行检验,其结果表明,提出的预测方法,预测精度明显优于BP算法,证明了这种方法的有效性和可靠性.     

基于字典学习的地震数据随机噪声压制算法

针对传统变换基函数难以获得地震数据最优的稀疏表示,提出基于字典学习的随机噪声压制算法,将地震数据分块,每一块包含多个地震记录道在一定采样时间段内波形的信息,利用自适应字典学习技术,以地震数据块为训练样本,根据地震数据邻近块中记录道相似的特点,构造超完备字典,稀疏编码地震数据,从而恢复数据的主要特征,压制随机噪声.实验表明算法具有较高的PSNR值,并且能较好的保持地震数据纹理复杂区域的局部特征.     

约束非线性规划的神经网络算法

神经网络具有内在大规模并行运算和快速收敛特性,它在最优化技术上的运用近年来受到广泛的重视。本提出一个新的求解一般约束非线性规划的神经网络模型,它具有全局收敛性和广泛的适用性,是求解一般非线性规划问题的新工具。理论分析和模拟计算均表明了模型的有效性。     

混沌神经网络优化算法用于求解无等待流水调度问题的最小最大完工时间

利用李小平等提出的相邻工件加工结束时间差矩阵,将求解无等待流水调度问题的最小最大完工时间(Makespan)问题映射为TSP问题,构造对应的能量函数,进而得到随机混沌神经网络(SCSA)算法.实验结果证明该混沌神经网络优化算法优于RAJ算法和GANRAJ算法.     

变尺度快速二阶非线性优化算法在神经网络辨识中的应用

神经网络是非线性系统建模与辨识的重要方法 ,反向传播 (BP)算法常常用在神经网络的权值训练中 ,但是 BP算法的收敛速度慢 .本文提出一种变尺度二阶快速优化方法 ,在这种方法中用二阶插值法来优化搜索学习速率 ,然后将这一方法应用于神经网络的辨识中 ,仿真研究表明新算法有更快的收敛速度和更好的收敛精度 .     

一种基于集成学习的试题多知识点标注方法

个性化试题推荐、试题难度预测、学习者建模等教育数据挖掘任务需要使用到学生作答数据资源及试题知识点标注,现阶段的试题数据都是由人工标注知识点。因此,利用机器学习方法自动标注试题知识点是一项迫切的需求。针对海量试题资源情况下的试题知识点自动标注问题,本文提出了一种基于集成学习的试题多知识点标注方法。首先,形式化定义了试题知识点标注问题,并借助教材目录和领域知识构建知识点的知识图谱作为类别标签。其次,采用基于集成学习的方法训练多个支持向量机作为基分类器,筛选出表现优异的基分类器进行集成,构建出试题多知识点标注模型。最后,以某在线教育平台数据库中的高中数学试题为实验数据集,应用所提方法预测试题考察的知识点,取得了较好的效果。     

一种加权的支持向量机及其在储层识别中的应用

标准支持向量机(SVM)抗噪声能力不强,当训练样本中存在有噪声或者野点时,会影响最优分类面的产生,最终导致分类结果出现偏差。针对这一问题,提出了一种考虑最小包围球的加权支持向量机(WSVM),给每个样本点赋予不同的权值,以此来降低噪声或野点对分类结果的影响。对江汉油田某区块的oilsk81,oilsk83和oilsk85三口油井的测井数据进行交叉验证,其中核函数采用了线性、指数和RBF这3种不同的核函数。测试结果显示,无论是在SVM还是在WSVM中,核函数选择RBF识别率都是最高的,同时提出的WSVM不受核函数的影响,识别稳定性好,且在交叉验证中识别率都能够达到100%。     

A Learning Algorithm for the Control of Continuous Action Set-Point Regulator Systems

The convergence properties for reinforcement learning approaches, such as temporal differences and Q-learning, have been established under moderate assumptions for discrete state and action spaces. In practice, however, many systems have either continuous action spaces or a large number of discrete elements. This paper presents an approximate dynamic programming approach to reinforcement learning for continuous action set-point regulator problems, which learns near-optimal control policies based on scalar performance measures. The continuous-action space (CAS) algorithm uses derivative-free line search methods to obtain the optimal action in the continuous space. The theoretical convergence properties of the algorithm are presented. Several heuristic stopping criteria are investigated and practical application is illustrated by two example problems—the inverted pendulum balancing problem and the power system stabilization problem.     

The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems

The statistical theories are not expected to generate significant conclusions, when applied to very small data sets. Knowledge derived from limited data gathered in the early stages is considered too fragile for long term production decisions. Unfortunately, this work is necessary in the competitive industry and business environments. Our previous researches have been aimed at learning from small data sets for scheduling flexible manufacturing systems, and this article will focus development of an incremental learning procedure for small sequential data sets. The main consideration concentrates on two properties of data: that the data size is very small and the data are time-dependent. For this reason, we propose an extended algorithm named the Generalized-Trend-Diffusion (GTD) method, based on fuzzy theories, developing a unique backward tracking process for exploring predictive information through the strategy of shadow data generation. The extra information extracted from the shadow data has proven useful in accelerating the learning task and dynamically correcting the derived knowledge in a concurrent fashion.     

A Learning Algorithm for Level Sets Weights in Weighted Level-based Averaging Method

The method which we call the Weighted Averaging Based on Levels (WABL) can be used to calculate the average representative of a fuzzy number. It utilizes weight coefficients for the level sets as well as the sides of a fuzzy number. We have developed an algorithm to obtain these coefficients. The most remarkable feature of this algorithm is that it makes use of the decision maker’s (DM) aggregation strategy.     

A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining

This paper presents a dual-objective evolutionary algorithm (DOEA) for extracting multiple decision rule lists in data mining, which aims at satisfying the classification criteria of high accuracy and ease of user comprehension. Unlike existing approaches, the algorithm incorporates the concept of Pareto dominance to evolve a set of non-dominated decision rule lists each having different classification accuracy and number of rules over a specified range. The classification results of DOEA are analyzed and compared with existing rule-based and non-rule based classifiers based upon 8 test problems obtained from UCI Machine Learning Repository. It is shown that the DOEA produces comprehensible rules with competitive classification accuracy as compared to many methods in literature. Results obtained from box plots and t-tests further examine its invariance to random partition of datasets. An erratum to this article is available at .     

A machine learning approach for individual claims reserving in insurance

Accurate loss reserves are an important item in the financial statement of an insurance company and are mostly evaluated by macrolevel models with aggregate data in run‐off triangles. In recent years, a new set of literature has considered individual claims data and proposed parametric reserving models based on claim history profiles. In this paper, we present a nonparametric and flexible approach for estimating outstanding liabilities using all the covariates associated to the policy, its policyholder, and all the information received by the insurance company on the individual claims since its reporting date. We develop a machine learning–based method and explain how to build specific subsets of data for the machine learning algorithms to be trained and assessed on. The choice for a nonparametric model leads to new issues since the target variables (claim occurrence and claim severity) are right‐censored most of the time. The performance of our approach is evaluated by comparing the predictive values of the reserve estimates with their true values on simulated data. We compare our individual approach with the most used aggregate data method, namely, chain ladder, with respect to the bias and the variance of the estimates. We also provide a short real case study based on a Dutch loan insurance portfolio.     

模糊灰色关联法在大牛地气田优选压裂井层中的应用

压裂是油气田开发的重要增产措施,选择合适的井层进行压裂至关重要.压裂选井时需要综合考虑多种不同的因素,而且各因素在不同程度上影响压裂效果,因素间又相互影响关联.为优选适宜压裂的井,运用灰色关联法综合考虑11种因素,用效果测度函数进行无量纲化,层次分析法求取因素权重,最后以带权的灰色关联度指标优选压裂井.通过实例验证了方法的可靠性与适用性,且计算简单,可指导压裂选井工作,并具有可推广性.