基于改进ABC与LS-SVM算法的电力负荷预测的研究

为了解决电力负荷的非线性等问题和帮助电力企业迅速地制定电力的预计交易量,提出一种建立在最小二乘支持向量机算法基础上的电力负荷预测方法。采用改进的ABC算法优化惩罚因子C和核系数σ,再将最优解赋给LS-SVM用以预测。仿真结果证明:基于改进ABC与LS-SVM算法的电力负荷预测方法具有较高的预测精度,更小的误差,是一种有效的预测方法。     

Maintaining the validity of simulation models using prediction intervals

This paper presents a methodology for maintaining the operational validity of simulation models of observable systems in order to support operational decisions. In this methodology, real-time system data are continuously compared against simultaneous prediction intervals on selected responses constructed using the simulation model. The methodology is illustrated through using a case example of a simulation model of a flexible manufacturing system. Different invalidating discrepancies between the model and the system are investigated. Results indicate that using nontraditional responses may lead to a faster detection of invalidating changes, the speed of detection is a function of the scope of the change, and the model may evolve with the system and continue to be used to guard against random changes.     

基于单个SnO2传感器的CO/H2混合气体定量分析

利用单个商品化SnO2传感器,对CO和H2及其混合气体进行分类识别和定量分析.对传感器进行温度调制,获得传感器在不同浓度CO、H2和CO/H2混合气体中的动态响应.对动态响应信号进行离散小波变换(DWT),选取小波系数作为特征,输入到支持向量机(SVM),实现了对CO、H2及其混合气体的识别(其中CO浓度100×10-6,H2浓度1 000×10-6),正确识别率超过96%.从动态响应曲线提取特征,利用SVM模型对CO/H2混合气体进行定量分析,准确地估计出CO的浓度.另外,采用相同样本集,对比SVM和BP算法, 结果显示SVM具有更优的泛化性能.可用的数据处理算法可移植到单片机实现,在气体传感器智能化的研究中具有潜在的应用价值.     

Implementation of hybrid ANN–PSO algorithm on FPGA for harmonic estimation

Harmonic estimation is the main process in active filters for harmonic reduction. A hybrid Adaptive Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm is being proposed for harmonic isolation. Originally Fourier Transformation is used to analyze a distorted wave. In order to improve the convergence rate and processing speed an Adaptive Neural Network Algorithm called Adaline has then been used. A further improvement has been provided to reduce the error and increase the fineness of harmonic isolation by combining PSO algorithm with Adaline algorithm. The inertia weight factor of PSO is combined along with the weight factor of Adaline and trained in Neural Network environment for better results. ANN–PSO provides uniform convergence with the convergence rate comparable that of Adaline algorithm. The proposed ANN–PSO algorithm is implemented on an FPGA. To validate the performance of ANN–PSO; results are compared with Adaline algorithm and presented herein.     

Improvement of identification of blast furnace ironmaking process by outlier detection and missing value imputation

The control of blast furnace ironmaking process requires model of process dynamics accurate enough to facilitate the control strategies. However, data sets collected from blast furnace contain considerable number of missing values and outliers. These values can significantly affect subsequent statistical analysis and thus the identification of the whole process, so it becomes much important to deal with these values. This paper considers a data processing procedure including missing value imputation and outlier detection, and examines the impact of processing to the identification of blast furnace ironmaking process. Missing values are imputed based on the decision tree algorithm and outliers are identified and discarded through a set of multivariate outlier detection methods. The data sets before and after processing are then used for identification. Two classic identification methods, N4SID (numerical algorithms for state space subspace system identification) and PEM (prediction error method) are considered and a comparative study is presented.     

基于过程神经元网络的时间序列预测方法

传统人工神经网络时间序列预测方法难以表达时间序列中的时间累积效应。为此,提出一种基于过程神经元网络的时间序列预测方法。采用双链结构的量子粒子群对过程神经元网络进行训练,以Mackey-Glass混沌时间序列预测为例进行实验。仿真结果表明,该方法的均方误差比普通神经网络低一个数量级。     

Generalization performance of support vector machines and neural networks in runoff modeling

Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. In this research, we present a new data-driven model called support vector machines (SVMs) based on structural risk minimization principle, which minimizes a bound on a generalized risk (error), as opposed to the empirical risk minimization principle exploited by conventional regression techniques (e.g. ANNs). Thus, this stat-of-the-art methodology for prediction combines excellent generalization property and sparse representation that lead SVMs to be a very promising forecasting method. Further, SVM makes use of a convex quadratic optimization problem; hence, the solution is always unique and globally optimal. To demonstrate the aforementioned forecasting capability of SVM, one-day lead stream flow of Bakhtiyari River in Iran was predicted using the local climate and rainfall data. Moreover, the results were compared with those of ANN and ANN integrated with genetic algorithms (ANN-GA) models. The improvements in root mean squared error (RMSE) and squared correlation coefficient (R²) by SVM over both ANN models indicate that the prediction accuracy of SVM is at least as good as that of those models, yet in some cases actually better, as well as forecasting of high-value discharges.     

An alternative approach for modelling and simulation of traffic data: artificial neural networks

It is assumed that there is a complicated relationship between the driver characteristics and involvement in traffic accidents. It is quite difficult to simulate the effects of these driver characteristics into the traffic accidents. The artificial neural networks (ANN) approach is proposed for training-predicting the database in this paper since it is a more flexible and assumption-free methodology. The networks are organised in different architectures and the results have been compared in order to determine the best fitting one. Finally, the best possible architecture is selected for a better representation of the survey data and the prediction of accident percentage. The predictions about the outputs for the inputs which are not used in the training of the ANN provide information about the drivers which cannot be reached in the database. The predictions are highly satisfactory and the ANNs have been found to be reliable processing systems for modelling and simulation in the traffic data assessments.     

激光与视觉融合的透视平面检测与深度预测研究

针对单一的激光传感器或视觉传感器无法检测到透视三维平面的问题,提出一种基于激光传感器与视觉传感器融合的透视平面检测与深度预测算法;首先采用透视平面检测网络,在二维彩色图像中对透视平面进行图像分割;其次应用单一图像反射去除算法,在分割得到的透视平面区域分离背景信息,并使用MegaDepth算法进行深度预测,得到相对深度图;最后结合激光传感器的深度数据,采用抽样一致性算法,计算深度标尺,并使用对透视平面进行深度赋值,将相对深度图转化为绝对深度图,进而完成对透视平面的深度预测;实验结果表明该算法能成功检测并分割透视平面,且能得到正确的透视平面绝对深度信息.     

Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model

The paper presents an integrated model of artificial neural networks (ANNs) and non-dominated sorting genetic algorithm (NSGAII) for prediction and optimization of quality characteristics during pulsed Nd:YAG laser cutting of aluminium alloy. A full factorial experiment has been conducted where cutting speed, pulse energy and pulse width are considered as controllable input parameters with surface roughness and material removal rate as output to generate the dataset for the model. In ANN–NSGAII model, back propagation ANN trained with Bayesian regularization algorithm is used for prediction and computation of fitness value during NSGAII optimization. NSGAII generates complete set of optimal solution with pareto-optimal front for outputs. Prediction accuracy of ANN module is indicated by around 1.5 % low mean absolute % error. Experimental validation of optimized output results less than 1 % error only. Characterization of the process parameters in pareto-optimal region has been explained in detail. Significance of controllable parameters of laser on outputs is also discussed.     

无线传感器网络信息检测的目标跟踪算法

目标跟踪定位算法通过簇头节点与汇聚节点之间的通信来定位预测目标位置,但在信息传递过程中会因中间路由出错或者外部攻击致使到达汇聚节点的信息出现错误或丢失,影响目标定位预测的精度。通过分析路由过程中可能产生误差的原因(例如乱序和丢包)以及受攻击之后数据的特征采取有效策略,在汇聚节点接收到信息之后进行检测,排除异常数据,防止错误信息的干扰。仿真实验表明,在非理想状态下加入信息检测机制之后相对于无检测机制的情况定位精度提高,跟踪轨迹更加精确。     

Visualization of neural-network gaps based on error analysis

Presents a methodology for detection of neural-network gaps (NNGs) based on error analysis and the visualization that is applicable to the n-dimensional I/O domain. The generalization problem in artificial neural networks (ANN) training is analyzed and the concept of NNGs is introduced. The NNGs are highly undesirable in ANN generalization and methods for detecting, analyzing, and eliminating them are necessary. Previous methods for NNG detection, based on two-dimensional (2-D) and three dimensional (3-D) visualization, were not applicable for ANNs with more than three inputs. Experiments demonstrate advantages of this new methodology, which allows better understanding of the NNG phenomena using a quantitative approach.     

Application of integrated soft computing techniques for optimisation of hybrid CO2 laser–MIG welding process

In this paper, artificial neural networks (ANNs), genetic algorithm (GA), simulated annealing (SA) and Quasi Newton line search techniques have been combined to develop three integrated soft computing based models such as ANN–GA, ANN–SA and ANN–Quasi Newton for prediction modelling and optimisation of welding strength for hybrid CO₂ laser–MIG welded joints of aluminium alloy. Experimental dataset employed for the purpose has been generated through full factorial experimental design. Laser power, welding speeds and wires feed rate are considered as controllable input parameters. These soft computing models employ a trained ANN for calculation of objective function value and thereby eliminate the need of closed form objective function. Among 11 tested networks, the ANN with best prediction performance produces maximum percentage error of only 3.21%. During optimisation ANN–GA is found to show best performance with absolute percentage error of only 0.09% during experimental validation. Low value of percentage error indicates efficacy of models. Welding speed has been found as most influencing factor for welding strength.     

DEVS methodology for evaluating time-constrained message routing policies

Due to its ability to support temporal issues of systems, discrete event simulation is widely applicable to real-time system design. This paper presents a methodology for the modeling and simulation of time-constrained message routing policies for hypercube interconnected real-time systems. The methodology is based on a framework called the DEVS (discrete event systems specification) formalism which supports modular and hierarchical specification of discrete event models. Within the methodology, we first develop DEVS specification for models for hypercube computers and experimental frames to measure the performance of alternative message routing policies. We then implement such specification in DEVSIM++, a C++-based modeling/simulation environment that implements the DEVS formalism. Simulations of various message routing policies are performed, and the performances of such policies are compared.     

Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting

In this paper methodologies are proposed to estimate the number of hidden neurons that are to be placed numbers in the hidden layer of artificial neural networks (ANN) and certain new criteria are evolved for fixing this hidden neuron in multilayer perceptron neural networks. On the computation of the number of hidden neurons, the developed neural network model is applied for wind speed forecasting application. There is a possibility of over fitting or under fitting occurrence due to the random selection of hidden neurons in ANN model and this is addressed in this paper. Contribution is done in developing various 151 different criteria and the evolved criteria are tested for their validity employing various statistical error means. Simulation results prove that the proposed methodology minimized the computational error and enhanced the prediction accuracy. Convergence theorem is employed over the developed criterion to validate its applicability for fixing the number of hidden neurons. To evaluate the effectiveness of the proposed approach simulations were carried out on collected real-time wind data. Simulated results confirm that with minimum errors the presented approach can be utilized for wind speed forecasting. Comparative analysis has been performed for the estimation of the number of hidden neurons in multilayer perceptron neural networks. The presented approach is compact, enhances the accuracy rate with reduced error and faster convergence.     

Prediction of quality using ANN based on Teaching‐Learning Optimization in component‐based software systems

The primary objective of our research work is to enhance the prediction of the quality of a component‐based software system and to develop an artificial neural network (ANN) model for the system reliability optimization problem. In this paper, we introduced the ANN‐supported Teaching‐Learning Optimization by transforming constraints to objective functions. Artificial neural network techniques are found to be powerful in the modeling software package quality metrics compared with the ancient statistical techniques. Therefore, by using the neural network, the quality characteristics of software components of the proposed work are predicted. A nonlinear differentiable transfer function of ANN used in the proposed approach is hyperbolic tangent sigmoid. A new efficient optimization methodology referred to as the Teaching‐Learning–based Optimization is proposed in this paper to optimize reliability and different cost functions. The weight values of the network are then adjusted consistent with a proposed optimization rule, therefore minimizing the network error. The proposed work is implemented in MATLAB by using the Neural Network Toolbox. The proposed work provides improved performance in terms of sensitivity, precision, specificity, negative predictive value, fall‐out or false positive rate, false discovery rate, accuracy, Matthews correlation coefficient, and rate of convergence.     

局部近邻标准化偏最小二乘的多模态间歇过程故障检测

本文针对多模态间歇过程数据多中心和模态方差差异明显的问题,提出了一种基于局部近邻标准化偏最小二乘方法.首先,采用统计模量方法处理间歇过程数据,再利用局部近邻标准化方法将统计模量后的训练数据进行高斯化处理,建立偏最小二乘监控模型,确定控制限;然后,同样对统计模量后的测试数据进行局部近邻标准化处理,再计算测试数据的高斯偏最小二乘监控指标,进行过程监视及故障检测.最后,通过数值实例和青霉素发酵过程验证方法有效性.实验结果表明所提方法解决了故障样本近邻集跨模态问题,对多模态数据具有更好的故障检测能力.     

Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models

This study investigates the ability of wavelet-artificial neural networks (WANN) for the prediction of short-term daily river flow. The WANN model is improved by conjunction of two methods, discrete wavelet transform and artificial neural networks (ANN) based on regression analyses, respectively. The proposed WANN models are applied to the daily flow data of Vanyar station, on the Ajichai River in the northwest region of Iran, and compared with the ANN and support vector machine (SVM) techniques. Mean square error (MSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for evaluating precision of the WANN, ANN and SVM models. Comparison results demonstrate that the WANN model performs better than the ANN and SVM models in short-term (1-, 2- and 3-day ahead) daily river flow prediction.

     

Robust tuning of bending-points detection algorithms in batch-operated processes: Application to Autothermal Thermophilic Aerobic Digesters

The literature shows a diversity of real-time algorithms for automatic detection of bending-points in batch-operated waste treatment systems. In this study a new methodology is proposed for tuning the parameters of these algorithms when uncertainty specifications are considered at the outset. In this method the effects of slow and fast dynamic responses on the shape of signal trajectories were treated separately in order to cover via simulation all possible operating scenarios for a real situation. Long-term uncertainty and steady-state simulations were combined to derive probability distributions for biomasses. These probability distributions were then merged with short-term uncertainty to run one-cycle random simulations with which to reproduce the entire diversity of signal trajectories. Finally, an optimisation problem was formulated in terms of the algorithm parameters. The methodology was satisfactorily applied to tune an algorithm for detection of bending-points in an Autothermal Thermophilic Aerobic Digestion (ATAD) process.     

Combination of artificial neural-network forecasters for predictionof natural gas consumption

The focus of this paper is on combination of artificial neural-network (ANN) forecasters with application to the prediction of daily natural gas consumption needed by gas utilities. ANN forecasters can model the complex relationship between weather parameters and previous gas consumption with the future consumption. A two-stage system is proposed with the first stage containing two ANN forecasters, a multilayer feedforward ANN and a functional link ANN. These forecasters are initially trained with the error backpropagation algorithm, but an adaptive strategy is employed to adjust their weights during online forecasting. The second stage consists of a combination module to mix the two individual forecasts produced in the first stage. Eight different combination algorithms are examined, they are based on: averaging, recursive least squares, fuzzy logic, feedforward ANN, functional link ANN, temperature space approach, Karmarkar's linear programming algorithm (1984) and adaptive mixture of local experts (modular neural networks). The performance is tested on real data from six different gas utilities. The results indicate that combination strategies based on a single ANN outperform the other approaches.