基于伪差分扰动的混合蛙跳算法研究

为了充分发掘混合蛙跳算法求解复杂优化问题的能力,提出了一种新颖的改进混合蛙跳算法.改进算法借鉴粒子群优化算法的速度更新方式,通过族群中随机个体、最优个体和最差个体间的位置关系来确定最差个体的更新步长;借鉴差分进化思想,通过伪差分变异产生虚拟个体来更新最差个体,以提高种群开拓能力.通过对四个典型测试函数的仿真实验表明,相比其他几种改进算法,改进算法以100％的概率找到了某些函数的理论最优值,寻优效果更好,收敛成功率更高.     

基于EMD和BP网络的轴承故障诊断方法

故障轴承的振动信号是非平稳信号,传统的非平稳信号分析手段存在许多不足;BP网络能够出色地解决传统识别模式难以解决的复杂问题。提出了经验模态分解(EMD)与BP神经网络相结合的滚动轴承故障诊断方法。采用EMD方法对振动信号进行分解,得到组成信号的多个内禀模态分量(IMF),提取重要的IMF分量的能量作为信号的特征量;采用BP网络作为模式分类器,对轴承的故障类型进行分类。经试验数据分析证明,该方法能够准确地对轴承故障进行诊断。     

改进的混合蛙跳移动机器人路径规划算法

为了实现移动机器人在障碍环境中的路径规划,提出一种改进的混合蛙跳算法(SFLA).改进算法在原算法基础上引入交叉操作,并在青蛙更新策略中充分利用学习机制;此外提出了一种带控制参数的产生新个体的方法代替原本的随机更新操作.把路径规划问题转换为最小化问题,基于环境中目标和障碍物的位置定义青蛙的适应度,机器人依次到达每次迭代中最好蛙的位置,从而实现最优路径规划.移动机器人仿真实验中,与基本蛙跳算法和其他智能算法相比,改进算法在规划时间和成功次数上均有很大的提高.实验结果表明了改进算法的有效性.     

基于改进BP网络算法的故障诊断分析

介绍BP神经网络结构和学习方法,针对误差反向传播神经网络模型学习收敛速度慢、容易陷入局部极小点等缺点,本文对BP网络模型进行了改进。对原始数据采用非线性的归一化函数,提出一种更加有效的学习率改进算法,提高了网络的收敛速度,采用了一种新的权值及阈值初始化方法,以避免训练时误差陷入局部极小解,并对改进BP算法与传统的BP算法进行比较,验证了该算法的优越性。     

改进神经网络的传感器故障诊断与容错处理

为提高神经网络对霍尔传感器发生故障诊断率,设计了一种改进神经网络模型,该模型由反向传播(BP)神经网络并行组成,利用算法对各BP神经网络输出进行加权整合,进而得到误差更小的输出结果,并将改进神经网络模型应用于无刷直流电机霍尔传感器故障诊断系统中,利用无位置传感器系统实现霍尔传感器故障容错处理。仿真结果表明:改进神经网络故障诊断准确率达到96%,高于传统BP神经网络,且容错控制系统能够显著降低霍尔传感器故障对电机转速的影响,使电机能够在霍尔传感器故障时正常稳定运行。     

An enhanced cost-aware mapping algorithm based on improved shuffled frog leaping in network on chips

The Journal of Supercomputing - Network on chip (NoC) has been of great interest in recent years. However, according to the recent studies, high communication cost has been raised as the one most...     

Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm

This paper reports a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox. A mathematical model is established to illustrate the sensor network optimization based on fault-sensor dependence matrix. The crossover and mutation operators of genetic algorithm (GA) are introduced into the update strategy of shuffled frog leaping algorithm (SFLA) and a new ID-SFLA is systematically developed. Numerical simulation results show that the ID-SFLA has an excellent global search ability and outstanding convergence performance. The ID-SFLA is applied to the sensor’s optimal selection for a gearbox. In comparison with GA and discrete shuffled frog leaping algorithm (D-SFLA), the proposed ID-SFLA not only poses an effective solving method with swarm intelligent algorithm, but also provides a new quick algorithm and thought for the solution of related integer NP-hard problem.     

Fuzzy lattice classifier and its application to bearing fault diagnosis

In this work, we present a novel classification scheme named fuzzy lattice classifier (FLC) based on the lattice framework and apply it to the bearing faults diagnosis problem. Different from the fuzzy lattice reasoning (FLR) model developed in literature, there is no need to tune any parameter and to compute the inclusion measure in the training procedure in our new FLC model. It can converge rapidly in a single pass through training patterns with a few induced rules. A series of experiments are conducted on five popular benchmark datasets and three bearing datasets to evaluate and compare the presented FLC with the FLR model as well as some other widely used classification methods. Experimental results indicate that the FLC yields a satisfactory classification performance with higher computation efficiency than other classifiers. It is very desirable to utilize the FLC scheme for on-line condition monitoring of bearings and other mechanical systems.     

改进混合蛙跳算法在蔬菜总黄酮软测量中的应用

针对蔬菜总黄酮化学物提取过程复杂、非线性和生物参数难以在线测量等特点,提出了基于改进混合蛙跳算法优化最小二乘支持向量机的蔬菜总黄酮软测量模型。该模型对标准混合蛙跳算法进行改进,采用反向学习的种群初始化策略,确保个体分布的均匀性;并根据群体适应度方差大小,动态调整变异概率,使算法避免陷入局部最优;最后采用改进的混合蛙跳算法对最小二乘支持向量机的参数进行寻优,实现蔬菜总黄酮软测量。仿真结果表明,基于改进混合蛙跳算法的最小二乘支持向量机软测量模型具有测量精度高,稳定性好的优点,有利于蔬菜总黄酮化学物测量工程的实际应用。     

改进型BP神经网络MRAS控制系统

在常规BP神经网络模型参考自适应控制器基础上采用改进型BP神经网络作为辨识器和控制器,组成新的模型参考神经网络自适应控制系统,利用改进型BP神经网络的优点弥补传统自适应方法的不足,使系统具有更强的鲁棒性,收敛更快,逼近精度更高的优点。仿真结果表明,该系统比传统BP神经网络模型参考自适应系统具有更好的稳定性和更快的响应速度。     

一种改进的神经网络机械故障诊断专家系统

针对传统BP神经网络训练中收敛速度较慢的缺点,提出一种基于L-M算法的神经网络应用于机械设备故障诊断的专家系统。论述了神经网络的专家系统结构,并以7216圆锥轴承试验研究为例,建立了基于该算法的故障诊断模型。仿真结果表明:该模型显著缩短了训练时间,具有较高的准确性。运用该神经网络专家系统进行机械故障诊断是有效的。     

MATLAB神经网络BP网络研究与应用

阐述了MATLAB神经网络,着重研究了其BP网络的网络结构,指出了BP算法的主要缺点,利用其工具箱中的函数对BP算法进行了改进。根据MATLAB神经网络BP网络的网络结构,提出了一种具有天气敏感性的基于快速BP算法的神经网络预测模型,并对电力短期负荷进行了预测。预测结果,证明了该算法的有效性。     

A modular neural network approach to fault diagnosis

Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation.     

BP神经网络在射频识别定位系统中的应用研究

为了提高采用射频识别技术进行定位的精度,针对无源标签射频识别技术及采用BP神经网络对其定位精度的改善进行了研究。首先建立了基于无源标签的射频识别定位系统,之后建立了相应的BP神经网络,并通过实验进行了验证。实验结果表明,在60 cm×50 cm的区域内,通过四角布置四个天线,利用信号强度作为输入信号,采用BP神经网络可以将定位误差控制在2 cm以内,平均欧几里得误差控制在1以内。说明采用BP神经网络可以改善射频识别定位技术的精度。     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.     

A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis

With the development of the condition-based maintenance techniques and the consequent requirement for good machine learning methods, new challenges arise in unsupervised learning. In the real-world situations, due to the relevant features that could exhibit the real machine condition are often unknown as priori, condition monitoring systems based on unimportant features, e.g. noise, might suffer high false-alarm rates, especially when the characteristics of failures are costly or difficult to learn. Therefore, it is important to select the most representative features for unsupervised learning in fault diagnostics. In this paper, a hybrid feature selection scheme (HFS) for unsupervised learning is proposed to improve the robustness and the accuracy of fault diagnostics. It provides a general framework of the feature selection based on significance evaluation and similarity measurement with respect to the multiple clustering solutions. The effectiveness of the proposed HFS method is demonstrated by a bearing fault diagnostics application and comparison with other features selection methods.     

A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration

One of the very important way to save the electrical energy in distribution system is network reconfiguration for loss reduction. This paper proposes a new hybrid evolutionary algorithm for solving the distribution feeder reconfiguration (DFR) problem. The proposed hybrid evolutionary algorithm is the combination of SAPSO (self-adaptive particle swarm optimization) and MSFLA (modified shuffled frog leaping algorithm), called SAPSO–MSFLA, which can find optimal configuration of distribution network. In the PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Therefore, a self-adaptive framework is proposed to improve the robustness of the PSO, also in the modified shuffled frog leaping algorithm (MSFLA) to improve the performance of algorithm a new frog leaping rule is proposed to improve the local exploration of the SFLA. The main idea of integrating SAPSO and MSFLA is to use their advantages and avoid their disadvantages. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization in minimum time.     

基于BP神经网络改进UKF的组合导航算法

针对在进近着陆的过程中,仪表着陆系统(ILS)易受到外界环境及空域的干扰,导致导航精度降低的问题,提出一种利用惯性导航系统(INS)与GBAS着陆系统(GLS)进行改进的组合导航算法,将组合导航系统输出位置信息之间的差值作为BP神经网络改进的无迹卡尔曼滤波器(UKF)的量测值,通过最优加权的方法得到系统的全局最优估计值。相比于传统的联邦滤波算法,该算法能有效降低测量噪声,减小飞机进近着陆时的误差,提高导航精度。     

基于神经网络的多层控制系统智能故障诊断

阐述了智能故障诊断技术的特点，针对复杂多层次控制系统，提出了一种分层模块化设计方法及模块间故障变量传播的搜索模式；探讨了基于神经网络与其它理论方法相结合的几种智能化诊断模式，对应用的方法，特点作了进一步分析。