基于改进PSO-BP神经网络的贮存可靠性预测

贮存可靠性是军事储备质量监测的重要环节,科学准确地预测贮存可靠度是现代化军事评估的必然要求。针对历史贮存数据,建立可靠度与年限的贮存可靠性预测模型,采用进化策略改进粒子群算法(PSO)优化BP神经网络进行贮存可靠性预测。通过数据扩充提高样本质量和数量,应用改进后的PSO算法优化BP神经网络的初始权值和阈值,提高网络的泛化能力。PSO算法较好的全局搜索能力与BP网络很强的局部搜索能力相结合,能够避免早熟现象,提高算法的收敛速度及预测精度。实验结果表明,改进的PSO-BP网络模型比PSO-BP和BP神经网络获得更好的预测性能。     

混沌粒子群神经网络在上证指数预测中的应用

上证指数预测是一个非常复杂的非线性问题,为了提高对上证指数预测的准确性,本文采用基于混沌粒子群(CPSO)算法对BP神经网络算法改进的方法来进行预测.BP神经网络算法目前已经应用到预测、聚类、分类等许多领域,取得了不少的成果.但自身也有明显的缺点,比如易陷入局部极小值、收敛速度慢等.用混沌粒子群算法改进BP神经网络算法的基本思想是用混沌粒子群算法优化BP神经网络算法的权值和阈值,在粒子群算法中加入混沌元素,提高粒子群算法的全局搜索能力.对上证指数预测的结果表明改进后的预测方法,具有更好的准确性.     

基于PSO_Trainlm BP模型的图像去噪研究

针对在使用BP模型进行图像去噪时,模型存在的对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.为了提高模型去噪效率,提出采用改进粒子群神经网络模型进行图像去噪.首先运用改进粒子群算法对BP神经网络权阈值进行初始寻优,再用trainlm BP算法对优化的网络权阈值进一步精确优化,随后建立基于粒子群算法的BP神经网络去噪模型,并将其应用到图像去噪研究中.仿真结果表明,新模型结合了粒子群算法的全局寻优能力和BP算法的局部搜索能力,减小了模型对初始权阈值的敏感性,有效防止了模型陷入局部极小值的可能,提高了图像去噪模型的速度和质量.     

基于CUDA的边界变异量子粒子群优化算法

针对量子粒子群优化算法面对复杂优化问题时,临近最优解的搜索阶段存在收敛速度慢、在边界附近全局搜索性差的问题,提出了基于CUDA的边界变异量子粒子群优化算法.GPU(图形处理器)以多颗密集的计算核心模拟粒子的搜索过程,利用并发的优势提升粒子搜索速度;边界变异则通过以随机概率将边界粒子扩散到更大的搜索域,增加种群的多样性,提升粒子群的全局搜索性.对若干优化算法的仿真实验表明,所提出方法具有较好的全局收敛性,且同等目标精度下,取得了较高的有效加速比.     

基于改进的思维进化算法与BP神经网络的AQI预测

思维进化算法(MEA)的趋同和异化操作带有太多的随机性,公告板的信息不能得到充分利用,使得效率下降,出现重复搜索.为了避免MEA算法的这个缺点,借鉴遗传算法(GA)和粒子群算法(PSO)的优点,提出改进的思维进化算法(MEA—PSO—GA).利用MEA—PSO—GA算法优化BP神经网络的初始权值和阈值进而预测太原的日常空气质量指数(AQI).通过与MEA—BP算法,MEA—PSO—BP算法和MEA—GA—BP算法比较,实验结果表明,提出的MEA—PSO—GA—BP算法在预测精度、误差率和可靠性方面搜索速度更优,更易于实现AQI预测,具有较好的有效性和可行性,有一定的现实意义.     

混沌遗传算法对BP神经网络的改进研究

BP神经网络算法是目前应用最广泛的一种神经网络算法,但有收敛速度慢和易陷入局部极小值等缺陷.本文利用混沌遗传算法(CGA)具有混沌运动遍历性、遗传算法反演性的特性来改进BP神经网络算法.该算法的基本思想是用混沌遗传算法对BP神经网络算法的初始权值和初始阈值进行优化.把混沌变量加入遗传算法中,提高遗传算法的全局搜索能力和收敛速度;用混沌遗传算法优化后得到的最优解作为BP神经网络算法的初始权值和阈值.通过实验观察,改进后的结果与普通的BP神经网络算法的结果相比,具有更高的准确率.     

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 .     

一种快速且全局收敛的BP神经网络学习算法

目前误差反向传播(BP)算法在训练多层神经网络方面有很多成功的应用.然而,BP算法也有一些不足:收敛缓慢和易陷入局部极小点等.提出一种快速且全局收敛的BP神经网络学习算法,并且对该优化算法的全局收敛性进行分析和详细证明.实证结果表明提出的算法比标准的BP算法效率更高且更精确.     

基于改进CS优化BP神经网络的降水量预测模型

针对传统BP神经网络易陷入局部极值和连接权值难以确定的问题,提出了一种基于融合PSO(Particle Swarm Optimization)和CS(Cuckoo Search)的混合算法优化设计BP神经网络(PCS-BP)的预测模型.该优化方法主要利用混合算法优秀的全局搜索能力和收敛速度设计优化BP神经网络的连接权值和网络结构,解决了BP神经网络由于参数随机取值引起的网络震荡和过拟合的问题,提高了预测模型的准确性.结合具体实例,分别采用BP神经网络、CS-BP模型和PCS-BP模型对汉中地区的月降水量进行预测,实验结果表明,PCS-BP的平均绝对误差(MAE)为0.3966,均方根误差(RMSE)为2.3793,平均绝对百分比误差(MAPE)为0.46%,均优于其他模型,具有较好的预测能力.     

基于粒子群优化的神经网络预测模型

BP学习算法多采用梯度下降法调整权值,针对其易陷入局部极小、收敛速度慢和易引起振荡的固有缺陷,提出了一种改进粒子群神经网络算法.其基本思想是:首先采用改进粒子群优化算法反复优化BP神经网络模型的权值参数组合,再用BP算法对得到的网络参数进一步精确优化,最后用得到精确的最优参数组合进行预测.实验结果表明,该算法在股指预测中的预测性能明显提高.     

Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels

A great deal of research has been done on production planning and sourcing problems, most of which concern deterministic or stochastic demand and cost situations and single period systems. In this paper, we consider a new class of multi-period production planning and sourcing problem with credibility service levels, in which a manufacturer has a number of plants and subcontractors and has to meet the product demand according to the credibility service levels set by its customers. In the proposed problem, demands and costs are uncertain and assumed to be fuzzy variables with known possibility distributions. The objective of the problem is to minimize the total expected cost, including the expected value of the sum of the inventory holding and production cost in the planning horizon. Because the proposed problem is too complex to apply conventional optimization algorithms, we suggest an approximation approach (AA) to evaluate the objective function. After that, two algorithms are designed to solve the proposed production planning problem. The first is a PSO algorithm combining the AA, and the second is a hybrid PSO algorithm integrating the AA, neural network (NN) and PSO. Finally, one numerical example is provided to compare the effectiveness of the proposed two algorithms.     

An approximation-based approach for fuzzy multi-period production planning problem with credibility objective

This paper develops a fuzzy multi-period production planning and sourcing problem with credibility objective, in which a manufacturer has a number of plants or subcontractors. According to the credibility service levels set by customers in advance, the manufacturer has to satisfy different product demands. In the proposed production problem, production cost, inventory cost and product demands are uncertain and characterized by fuzzy variables. The problem is to determine when and how many products are manufactured so as to maximize the credibility of the fuzzy costs not exceeding a given allowable invested capital, and this credibility can be regarded as the investment risk criteria in fuzzy decision systems. In the case when the fuzzy parameters are mutually independent gamma distributions, we can turn the service level constraints into their equivalent deterministic forms. However, in this situation the exact analytical expression for the credibility objective is unavailable, thus conventional optimization algorithms cannot be used to solve our production planning problems. To overcome this obstacle, we adopt an approximation scheme to compute the credibility objective, and deal with the convergence about the computational method. Furthermore, we develop two heuristic solution methods. The first is a combination of the approximation method and a particle swarm optimization (PSO) algorithm, and the second is a hybrid algorithm by integrating the approximation method, a neural network (NN), and the PSO algorithm. Finally, we consider one 6-product source, 6-period production planning problem, and compare the effectiveness of two algorithms via numerical experiments.     

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean–variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.     

Evaluation of fuzzy regression models by fuzzy neural network

In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy linear and nonlinear regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

Fuzzy polynomial regression with fuzzy neural networks

Recently, fuzzy linear regression is considered by Mosleh et al. [1]. In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy polynomial regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

基于GA-BP的模糊神经网络控制器与Elman辨识器的系统设计

提出了一种基于神经网络的模糊控制系统 ,该系统由模糊神经网络控制器和模型辨识网络组成 .文中介绍了模糊神经网络控制器采用遗传算法离线优化与 BP算法在线调整 ,给出了具体控制算法 ,推导了变形 Elmam网络的系统辨识算法 .仿真结果表明了此法的可行性和有效性 .     

An architecture of fuzzy neural networks for linguistic processing

Fuzzy sets have been used successfully in order to deal with imprecise data, linguistic terms or not well-defined concepts. Recently, considerable effort has been made in the direction of combining the neural network approach with fuzzy sets. In this paper a fuzzy feed-forward neural network, able to process trapezoidal fuzzy sets, has been investigated. Normalized trapezoidal fuzzy sets have been considered. The fuzzy generalized delta rule with different back-propagation algorithms is discussed. The more interesting and characteristic property of the proposed architecture is the ability of each node to process fuzzy sets or linguistic terms, preserving the simplicity of the back-propagation algorithm. Consequently, the resulting architecture is able to cope with problems in which the input parameters and the desired targets are described by linguistic terms. This methodology has the further interesting characteristic of being able to operate at the linguistic level rather than at the numerical level, that is it can work at a higher data abstraction level. An example in computerized electrocardiography will be illustrated in order to test the proposed approach.     

模糊ART神经网络在运动目标识别中的应用

本文在讨论模糊ＡＲＴ神经网络及其算法的基础上，研究和提出了一种三维运动目标识别方法，利用模糊ＡＲＴ神经网络对运动目标的目标侧面图形进行学习和模式识别。模拟实验表明了该方法的有效性。     

Evaluation of fully fuzzy matrix equations by fuzzy neural network

In this paper, a new hybrid method based on fuzzy neural network for approximate solution of fully fuzzy matrix equations of the form AX=D$\mathit{AX}=D$, where A and D are two fuzzy number matrices and the unknown matrix X is a fuzzy number matrix, is presented. Then, we propose some definitions which are fuzzy zero number, fuzzy one number and fuzzy identity matrix. Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy matrix equations and fuzzy neural network. It is noted that the uniqueness of the calculated fuzzy inverse matrix is not guaranteed. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate solution of fuzzy matrix equations that supposedly has a unique fuzzy solution, a simple algorithm from the cost function of the fuzzy neural network is proposed. To illustrate the easy application of the proposed method, numerical examples are given and the obtained results are discussed.