两阶段动态多粒子群协作优化

为了解决粒子群优化（Particle Swarm Optimization,PSO）容易陷入到局部最优的问题,提出一种两阶段动态多粒子群协作优化算法.算法中包含一个主粒子群和多个从粒子群,每个从粒子群都搜索部分问题域,主粒子群协调各从粒子群向最优解收敛并获得搜索到的最优解.在第一阶段,在粒子少的问题域产生新的从粒子群,从而确保粒子比较好地覆盖问题域.在第二阶段,删除同一子区域中位置重叠的从粒子群,减少搜索时间.用五个测试函数与两层粒子群优化（Two-layer Particle Swarm Optimization,TLPSO）进行了比较,结果表明此算法能在高维多峰函数优化时获得更好的解.     

基于混合PSO小波网络的网络异常检测

提出一种用新型的进化学习算法训练的小波神经网络(WNN).这种新型的进化学习算法是基于粒子群算法(PSO)和共轭下降法(CG)提出的.以往,将粒子群算法用于神经网络的训练一般是可行的.因为粒子群算法相比于其他的优化算法,具有相对简单的结构和快速的收敛速度,然而,由于粒子的搜索坍塌速度过快而导致粒子停滞这种潜在的危险.粒子的持续停滞使搜索结果很难达到全局最优,甚至会陷入局部最优.为了克服粒子群算法缺点提出了改进的混合算法.通过对KDD 99数据集的实验表明,利用新型混合算法训练的小波神经网络对于异常检测具有很高的异常检测率并且又较低的误判率.可见,该方法对于网络异常检测是有效的.     

Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution

This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables. The proposed MODE learning algorithm adopts an evolutionary learning method to optimize the controller parameters. For design optimization, a new criterion is introduced. A hardware-in-the loop control technique is developed and applied to the designed ANFN controller using the MODE learning algorithm. The proposed ANFN controller with the MODE learning algorithm (ANFN-MODE) is used in two practical applications—the planetary-train-type inverted pendulum system and the magnetic levitation system. The experiment is developed in a real-time visual simulation environment. Experimental results of this study have demonstrated the robustness and effectiveness of the proposed ANFN-MODE controller.      

Evolutionary fuzzy neural networks for hybrid financial prediction

In this paper, an evolutionary fuzzy neural network using fuzzy logic, neural networks (NNs), and genetic algorithms (GAs) is proposed for financial prediction with hybrid input data sets from different financial domains. A new hybrid iterative evolutionary learning algorithm initializes all parameters and weights in the five-layer fuzzy NN, then uses GA to optimize these parameters, and finally applies the gradient descent learning algorithm to continue the optimization of the parameters. Importantly, GA and the gradient descent learning algorithm are used alternatively in an iterative manner to adjust the parameters until the error is less than the required value. Unlike traditional methods, we not only consider the data of the prediction factor, but also consider the hybrid factors related to the prediction factor. Bank prime loan rate, federal funds rate and discount rate are used as hybrid factors to predict future financial values. The simulation results indicate that hybrid iterative evolutionary learning combining both GA and the gradient descent learning algorithm is more powerful than the previous separate sequential training algorithm described in.     

基于神经网络集成的微带天线谐振频率建模

谐振频率是微带天线设计过程中最重要的一个参数,直接决定设计的成败.提出基于十进制粒子群优化(DePSO)算法和二进制粒子群优化(BiPSO)算法的选择性神经网络集成方法,通过粒子群优化(PSO)算法合理选择组成神经网络集成的各个神经网络,使个体间保持较大的差异度,减小"多维共线性"和样本噪声的影响.为有效保证PSO算法的粒子多样性,在迭代过程中加入混沌变异策略.仿真试验表明:混沌PSO算法可以有效提高神经网络集成的泛化能力,基于混沌PSO算法的选择性神经网络集成所建立的微带天线的谐振频率模型好于此问题的已有结论.     

粒子群优化的小波网络在飞参数据压缩中的应用

飞参数据压缩是减少飞参数据的存储空间和传输通信流量的关键。针对飞参数据的特点,提出了一种基于粒子群优化的小波神经网络近无损压缩算法。该算法将小波网络参数作为原始数据的重构信息,在小波神经网络BP算法的基础上,引入粒子群优化算法,克服了粒子群优化算法的早熟收敛,增强了小波神经网络学习算法的全局搜索能力,提高了网络收敛速度;同时将重构误差作为启发信息,在保证较小失真度的情况下,通过粒子的迭代寻求最优的小波神经网络结构。飞参数据压缩仿真实验结果表明了算法的可行性和有效性,可以获得较高的压缩比和较小的重构误差。     

求解连续空间优化问题的量子粒子群算法

为提高粒子群算法的搜索能力和优化效率并避免早熟收敛,将量子进化算法融合到粒子群算法中,提出一种求解连续空间优化问题的量子粒子群优化算法.用量子位的概率幅对粒子位置编码,用量子旋转门实现粒子移动,完成粒子搜索;用量子非门实现变异,提高种群多样性.因每个量子位有两个概率幅,故每个粒子同时占据空间两个位置,在粒子数目相同时,能加速粒子的搜索进程.实验结果表明,本算法优于基本粒子群算法.     

粒子群算法优化神经网络结构的研究

针对BP神经网络初始权阈值确定的随机性和隐含层节点数的不确定性,通过利用十进制粒子群优化算法（DePSO）和二进制粒子群优化算法（BiPSO）,同时优化神经网络的初始权阈值和结构。通过粒子群优化算法首先确定一个较好的搜索空间,然后在这个解空间里利用BP算法对网络进行训练和学习,搜索出最优解。通过函数拟合数值实验对该模型来进行训练和测试,相比其他算法,该模型可以获得较高的预测精度,结果表明该方法是可行的。     

基于小波变异本质粒子群和模糊熵的图像分割

针对本质粒子群(BBPSO)算法存在易陷入局部最优以及过早收敛的缺点,提出了一种基于小波变异(WM)BBPSO(WMBBPSO)和模糊熵的图像分割算法,利用WMBBPSO搜索使图像模糊熵最大的参数值,得到模糊参数的最优组合,进而确定图像的分割阈值。通过与其它两种BBPSO算法的分割结果比较表明,该算法取得了令人满意的分割结果,算法运算时间较小,能够满足对煤尘浓度实时精确测量的要求。     

基于PSO-RBF神经网络的模拟电路故障诊断研究

针对模拟电路故障诊断的难点和传统诊断方法的不足之处,提出了一种基于PSO算法优化的RBF神经网络模拟电路故障诊断方法。为了约简网络结构从而提高诊断效率,采用主成分分析方法对故障特征进行有效提取。针对RBF网络传统训练算法中隐层节点中心及基函数宽度选取困难问题,提出采用PSO算法来优化训练RBF网络,以提高网络的训练速度和泛化性能。最后,通过电路仿真对所提方法的有效性进行了验证。     

Nonlinear system modeling by competitive learning and adaptivefuzzy inference system

Modeling nonlinear systems by neural networks and fuzzy systems encounters problems such as the conflict between overfitting and good generalization and low reliability, which requires a great number of fuzzy rules or neural nodes and uses very complicated learning algorithms. A new adaptive fuzzy inference system, combined with a learning algorithm, is proposed to cope with these problems. First, the algorithm partitions the input space into some local regions by competitive learning, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares (RLS). In the learning algorithm, the key role of the decision boundaries is highly emphasized. To demonstrate the validity of the proposed learning approach and the new adaptive fuzzy inference system, four examples are studied by the proposed method and compared with the previous results     

模拟电路故障诊断的神经网络优化方法综述

对故障诊断和模拟电路的特点做了简要介绍,分析了故障诊断的现状和相关研究方法。针对目前用于模拟电路故障诊断的神经网络,阐述了优化神经网络的方法--小波变换、遗传算法、模糊理论、粒子群算法和聚类算法等,并对未来的发展方向进行了展望。     

基于改进粒子群算法的多UAV协同侦察任务规划

针对多无人机（UAV）协同侦察的任务规划问题,充分考虑侦察目标的侦察分辨率和时间窗约束,建立了数学模型;提出了一种改进的粒子群算法,使得粒子群能够较均匀地在问题空间内搜索,避免陷入局部极值,在保持传统PSO算法快速收敛的同时,加强了算法局部搜索能力。基于该模型和优化算法,制定了合理的多UAV协同侦察任务计划,使得多UAV协同侦察任务在满足任务要求、平台性能和战场约束的条件下具有最小代价和最优作战效能。     

基于Zernike矩和PSO算法的摄像机神经网络标定

提出了一种新的基于Zernike矩和粒子群(PSO)算法的摄像机BP神经网络标定方法。首先,利用Zernike矩和曲率不变性求取圆形标定模板中心的亚像素坐标,提高神经网络训练数据的精度;其次,利用PSO算法优化网络的初始权重和阈值,提高网络的收敛速度和泛化能力。实验结果表明,该方法在X轴和Y轴方向的测量误差小于0.06 mm,整个测试集均方根误差为0.194 mm,证明了该方法的有效性。     

粒子群优化BP-PID的矿井提升机调速系统

传统PID控制器在矿井提升机变频调速系统应用中,由于控制参数固定且不易整定,导致电机转速超调大、电磁转矩和转子磁链脉动大,进而出现矿井提升机调速系统控制效果差的问题。针对这一问题,文中提出一种改进粒子群优化BP神经网络PID控制器的算法。由于BP神经网络算法存在收敛速度慢和极易陷入局部最优的缺点,现将粒子群算法收敛速度快和全局最优特性与神经网络结合,并通过设计神经网络收敛系数进一步加快收敛速度。仿真结果表明,粒子群优化的神经网络控制效果比神经网络好,且效果明显优于传统PID控制器;相较于神经网络PID控制器,矿井提升机转速调节系统稳速调节速度明显提高;与传统PID控制器相比,电机电磁转矩和转子磁链脉动明显降低,具有较强的稳定性和鲁棒性。     

基于优化神经网络算法的电离层foF2预测

为了提高基于反向传输（back propagation,BP）神经网络的电离层f_oF₂预测的精度,采用了一种改进粒子群优化神经网络的方法,对BP网络的初始权值进行优化,防止出现神经网络训练中的局部最优.通过比较基于粒子群优化的神经网络预测结果与遗传算法优化的神经网络预测结果,我们发现对于BP神经网络,两种方法都有很好的性能.此外,和电离层经验模型国际参考电离层模型（international reference ionosphere 2016,IRI2016）结果进行对比,结果表明,本文提出的自适应变异粒子群（adaptive mutation particle swarm optimization,AMPSO）优化神经网络能有效提高f_oF₂的预测精度,并在低纬地区有更好的预测效果.     

Cluster based wireless sensor network routing using artificial bee colony algorithm

Due to recent advances in wireless communication technologies, there has been a rapid growth in wireless sensor networks research during the past few decades. Many novel architectures, protocols, algorithms, and applications have been proposed and implemented. The efficiency of these networks is highly dependent on routing protocols directly affecting the network life-time. Clustering is one of the most popular techniques preferred in routing operations. In this paper, a novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time. Artificial bee colony algorithm, simulating the intelligent foraging behavior of honey bee swarms, has been successfully used in clustering techniques. The performance of the proposed approach is compared with protocols based on LEACH and particle swarm optimization, which are studied in several routing applications. The results of the experiments show that the artificial bee colony algorithm based clustering can successfully be applied to WSN routing protocols.     

基于量子粒子群算法的移动节点覆盖优化

提出了基于量子粒子群的无线传感器网络覆盖优化算法.由于在量子空间中粒子满足集聚态性质完全不同,使得该算法可以在整个可行区域内搜索.全局搜索能力远远优干基本粒子群,克服了粒子群算法容易陷入局部最优的缺点.仿真结果表明,该算法比基本粒子群算法拥有更好的覆盖优化效果。     

基于改进粒子群优化算法nMOSFET寿命预测

对于电子器件寿命预测问题,文章提出了基于改进粒子群优化算法的BP神经网络电子器件寿命预测方法。首先对nMOSFET元件在不同应力条件下进行寿命试验,根据试验测试获得的寿命数据,得出对应的可靠性。文章通过结合改进粒子群优化算法和BP神经网络结合,建立电子器件寿命预测模型,应用该模型对相同应力条件的电子器件寿命进行预测,同时对应力加速条件下寿命的预测。通过试验证明,该算法具有更强的非线性拟合能力和更高的准确率。     

Cloud security situation prediction method based on grey wolfoptimization and BP neural network

Aiming at the accuracy and error correction of cloud security situation prediction, a cloud security situation prediction method based on grey wolf optimization (GWO) and back propagation (BP) neural network is proposed.Firstly, the adaptive disturbance convergence factor is used to improve the GWO algorithm, so as to improve theconvergence speed and accuracy of the algorithm. The Chebyshev chaotic mapping is introduced into the positionupdate formula of GWO algorithm, which is used to select the features of the cloud security situation prediction dataand optimize the parameters of the BP neural network prediction model to minimize the prediction output error.Then, the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm toincrease the learning efficiency and accuracy of BP neural network. Finally, the real data sets of Tencent cloudplatform are predicted. The simulation results show that the proposed method has lower mean square error (MSE)and mean absolute error (MAE) compared with BP neural network, BP neural network based on genetic algorithm(GA-BP), BP neural network based on particle swarm optimization (PSO-BP) and BP neural network based onGWO algorithm (GWO-BP). The proposed method has better stability, robustness and prediction accuracy.