Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.     

混合粒子群优化算法优化前向神经网络结构和参数

提出了综合利用粒子群优化算法（PSO）和离散粒子群优化算法（D-PSO）同时优化前向神经网络结构和参数的新方法。该算法使用离散粒子群优化算法优化神经网络连接结构,用多维空间中0或1取值的粒子来描述所有可能的神经网络连接,同时使用粒子群优化算法优化神经网络权值。将经过该算法训练的神经网络应用于故障诊断,能够有效消除冗余连接结构对网络诊断能力的影响。仿真试验的结果表明,相比遗传算法等其他算法,该算法能够有效改善神经网络结构和参数的优化效率,提高故障模式识别的准确率。     

Modeling of a proton exchange membrane fuel cell based on the hybrid particle swarm optimization with Levenberg–Marquardt neural network

This paper presents a nonlinear modeling approach of a proton exchange membrane fuel cell (PEMFC) based on the hybrid particle swarm optimization with Levenberg–Marquardt algorithm neural network (PSO-LM NN). The PSO algorithm converges rapidly during the initial stages of a global search, while it becomes extremely slow around the global optimum. On the contrary, the LM algorithm can achieve faster convergent speed around the global optimum, while it is prone to being trapped in the local minimum. Therefore the hybrid algorithm with a transition from PSO search to LM training is proposed to train the weights and thresholds of neural network, which aims to exploit the advantage of the both algorithms. An accurate mathematical model is an extremely useful tool for the fuel cell design, and neural network is an excellent optional tool for complex nonlinear dynamic system modeling such as PEMFC. In the paper, firstly a highly reduced PEMFC dynamic physical model is established to generate the data for the PSO-LM NN model training and validation, and then the neural network nonlinear autoregressive model based on the PSO-LM algorithm is applied in modeling PEMFC voltage and temperature model, and finally the validation test result demonstrates that the trained PSO-LM NN model can efficiently approach the dynamic behavior of a PEMFC.     

Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO

In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms (GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy inference systems are used to estimate the type-2 fuzzy weights of backpropagation neural networks. Simulation results and a comparative study among neural networks with type-2 fuzzy weights without optimization of the type-2 fuzzy inference systems, neural networks with optimized type-2 fuzzy weights using genetic algorithms, and neural networks with optimized type-2 fuzzy weights using particle swarm optimization are presented to illustrate the advantages of the bio-inspired methods. The comparative study is based on a benchmark case of prediction, which is the Mackey-Glass time series (for τ = 17) problem.     

改进PSO优化BP神经网络的混沌时间序列预测

为提高BP神经网络预测模型的预测准确性,提出了一种基于改进粒子群算法优化BP神经网络的混沌时间序列预测方法。引入自适应变异算子对陷入局部最优的粒子进行变异,改进了粒子群算法的寻优性能; 利用改进粒子群算法优化BP神经网络的权值和阈值,训练BP神经网络预测模型求得最优解。将该预测方法应用到几个典型的非线性系统的混沌时间序列进行有效性验证,结果表明了该方法对典型混沌时间序列具有更好的非线性拟合能力和更高的预测准确性。     

粒子群算法优化BP神经网络的粉尘浓度预测

对综采工作面粉尘浓度预测的方法是建立BP神经网络预测模型.为了提高算法的拟合能力及预测的准确度,使用粒子群算法对目标函数进行改进,即将粒子群算法寻到的最优权值和阈值应用于神经网络预测模型求综采工作面粉尘浓度.比较分析新的预测模型与常用的灰色模型以及标准的BP神经网络算法,结果表明粒子群优化的神经网络算法的拟合能力和预测的准确率显著提高.     

基于粒子群优化BP神经网络的可靠性评估模型

CPU的可靠性对计算机系统至关重要。针对神经网络等方法在可靠性分析与评估中参数优化困难、模型评估精度不够准确等问题,提出一种基于粒子群优化BP神经网络的可靠性评估模型。该模型利用由正弦映射优化的PSO算法对BP神经网络的权值和阈值进行优化,提高BP神经网络的收敛速度以及评估精度。基于CPU中各功能模块的可靠度,根据改进的BP神经网络模型建立CPU的可靠性评估模型,通过模型训练与测试完成对CPU的可靠性评估。通过对比实验,验证该模型对辐射环境下CPU可靠性评估的有效性和准确性。     

Hybrid GMDH-type modeling for nonlinear systems: Synergism to intelligent identification

This paper presents a novel hybrid GMDH-type algorithm which combines neural networks (NNs) with an approximation scheme (self-organizing polynomial neural network: SOPNN). This composite structure is developed to establish a new heuristic approximation method for identification of nonlinear static systems. NNs have been widely employed to process modeling and control because of their approximation capabilities. And SOPNN is an analysis technique for identifying nonlinear relationships between the inputs and outputs of such systems and builds hierarchical polynomial regressions of required complexity. Therefore, the combined model can harmonize NNs with SOPNN and find a workable synergistic environment. Simulation results of the nonlinear static system are provided to show that the proposed method is much more accurate than other modeling methods. Thus, it can be considered for efficient system identification methodology.     

PSO优化算法飞机操纵面故障辨识研究

针对飞机复杂的非线性操纵面故障系统,建立故障模型,提取各种故障数据,并将粒子群优化算法应用于BP网络系统,提出了一种基于粒子群优化神经网络的故障诊断方法;该方法分阶段实施神经网络的训练,有效地加强了算法的全局搜索能力,采用PSO算法优化了传播中的权值和阈值,弥补了BP算法收敛速度慢和易陷入局部极小点的不足,提高了故障模式识别的能力;实验结果表明该方法加快了神经网络的学习收敛速度,提高了故障模式的识别正确率,对飞机操纵面的各种典型故障都能有效加以辨识。     

Advanced self-organizing polynomial neural network

In this paper, we introduce a concept of advanced self-organizing polynomial neural network (Adv_SOPNN). The SOPNN is a flexible neural architecture whose structure is developed through a modeling process. But the SOPNN has a fatal drawback; it cannot be constructed for nonlinear systems with few input variables. To relax this limitation of the conventional SOPNN, we combine a fuzzy system and neural networks with the SOPNN. Input variables are partitioned into several subspaces by the fuzzy system or neural network, and these subspaces are utilized as new input variables to the SOPNN architecture. Two types of the advanced SOPNN are obtained by combining not only the fuzzy rules of a fuzzy system with SOPNN but also the nodes in a hidden layer of neural networks with SOPNN into one methodology. The proposed method is applied to the nonlinear system with two inputs, which cannot be identified by conventional SOPNN to show the performance of the advanced SOPNN. The results show that the proposed method is efficient for systems with limited data set and a few input variables and much more accurate than other modeling methods with respect to identification error.     

基于最小不确定性神经网络的茶味觉信号识别

提出了一种基于最小不确定性神经网络方法的味觉信号识别模型,使用贝叶斯概率理论和粒子群优化算法(PSO),快速而有效地确定网络结构参数,实现了对10种茶味觉信号的识别,实验结果表明了将该模型引入到茶味觉信号识别的可行性和有效性。     

模拟退火PSO的神经网络的网络流量预测模型

为解决网络流量时间序列的预测问题,针对传统BP神经网络的网络流量时间序列预测模型容易陷入局部极小值的不足,提出一种基于模拟退火的微粒群算法训练神经网络的网络流量时间序列预测模型.将模拟退火算法和基本粒子微粒群算法相结合,设计出一种基于模拟退火的微粒群算法.利用基于模拟退火微粒群算法优化BP神经网络的权值和阀值,对实际采集的网络流量时间序列进行建模.实验结果表明,基于模拟退火的微粒群算法训练的神经网络具有较高的预测效果,相对于传统的神经网络模型具有更高的预测精度和良好的自适应性.     

钢包炉配料PSO-BP-PID 控制研究

针对钢包精炼炉( Ladle Ｒefining Furnace) 又称LF 炉,配料加料过程的惯性、时滞、非线性等控制特性,设计了一种基于微粒群优化算法( Particle Swarm Optimization,PSO) 、误差反向传播( Back Propagation,BP) 神经网络以及比例－ 积分－ 微分( PID) 的复合控制算法PSO-BP-PID,并将该复合算法应用于150 t 钢包精炼炉配料称重控制系统中,实现配料称重过程的智能控制。PSO-BP-PID 算法利用微粒群优化算法的全局寻优特性,优化BP 神经网络的初始权值以提高神经网络的收敛性; 采用经微粒群算法优化后的BP 神经网络在线实时调整PID参数。通过基于PSO 和BP 网络的PID 控制器实时控制钢包精炼沪的配料过程。仿真实验和运行实验结果表明,PSO-BP-PID 算法的控制效果优于单一PID 算法的控制效果。采用PSO-BPPID算法的钢包炉配料系统后,明显提高了配料精度,有效地解决了配料称重过程中速度与精度的矛盾。     

Solution of the 2-dimensional Bratu problem using neural network,swarm intelligence and sequential quadratic programming

In this paper, stochastic techniques have been developed to solve the 2-dimensional Bratu equations with the help of feed-forward artificial neural networks, optimized with particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms. A hybrid of the above two algorithms, referred to as the PSO-SQP method is also studied. The original 2-dimensional equations are solved by first transforming them into equivalent one-dimensional boundary value problems (BVPs). These are then modeled using neural networks. The optimization problem for training the weights of the network has been addressed using particle swarm techniques for global search, integrated with an SQP method for rapid local convergence. The methodology is evaluated by applying on three different test cases of BVPs for the Bratu equations. Monte Carlo simulations and extensive analyses are carried out to validate the accuracy, convergence and effectiveness of the schemes. A comparative study of proposed results is made with available exact solution, as well as, reported numerical results.     

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.      

基于遗传算法的GMDH网络模型及其应用

传统的数据处理群方法(Group method of data handling,GMDH)在结构上具有自组织和全局选优的特性,非常适合进行非线性数据的拟合.但由于在传统GMDH网络建模是用最小二乘法来辨识参数,常常使得模型预测效果不理想.遗传算法是一种有效的搜索和优化方法.它具有自适应搜索、渐进式寻优、并行式搜索、通用性强等特点,论文将遗传算法引入GMDH网络,用遗传算法辨识部分描述式的系数,建立了基于遗传算法的GMDH网络模型.并将该模型应用于一组实测时间序列的预测研究.计算机仿真结果表明,模型预测效果令人满意.     

基于PSO算法的动态模块化神经网络结构设计

针对模块化神经网络结构设计过程中子网络输出不能最优集成的问题,提出一种基于粒子群算法的动态模块化神经网络.首先,该网络采用数据密度辨识样本分布空间,并更新数据中心;然后,根据输入数据激活相应的子网络,利用PSO算法寻找子网络的最优网络贡献度,并依据贡献度计算子网络的输出权值;最后优化模块化神经网络的集成输出.通过对非线性函数和时变系统的逼近实验,验证了集成网络中子网络数目可以根据任务动态调整,网络输出的集成权值能够通过PSO算法寻找到最优值,并且训练精度和自适应能力较其他算法均有一定的提高.     

粒子群可拓的新冠肺炎建模与仿真

本文提出了一种基于粒子群可拓神经网络预测模型.根据国外近段时间每日新增新冠肺炎确诊人数,利用可拓神经网络模型对国外日新增新冠肺炎确诊人数进行预测,并利用粒子群算法(PSO)对权值进行优化,最后与LSSVM、ABC-LSSVM及PSO-LSSVM模型进行比较.结果表明:采用文中提出的粒子群可拓神经网络模型拟合效果较好,精...     

基于小波神经网络的污水处理厂出水水质预测

在分析传统污水处理厂出水水质预测方法的基础上,提出一种核主元分析和小波神经网洛相结合的预测新方法。首先利用核主元分析实现输入变量的降维和去相关,然后运用小波神经网络建立预测模型。采用统计学理论的中的结构风险最小化原则为目标来训练网络的结构,采用自适应正交最小二乘法来训练网络权值,该方法最大限度地保证了网络的泛化能力。实验结果表明,该预测模型具有预测精度高,使用方便等优点。     

Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.