基于概率的自适应学习预测策略

动态电源管理技术降低系统功耗的主要办法是根据工作负载的变化动态地切换目标设备工作模式。针对自适应学习树模型的缺陷,提出基于概率的自适应学习预测策略,通过概率描述设备行为,能够提高预测正确率,从而达到系统功耗与性能之间的优化平衡。基于概率的自适应学习预测策略是一种集预测、控制、反馈为一体的预测策略。实验结果表明,该预测策略具有较好的稳定性,与其他预测策略相比可以进一步降低系统的功耗。     

应用模糊神经网络进行负荷预测的研究

应用模糊神经网络实现的预测系统通过对历史数据的自适应学习获得初始的模糊 预测模型,借助等价结构的ANN基于实时数据的梯度信息对系统参数进行BP训练,具有较 强的适应性和自学习能力.以电力短期负荷预测(STLF)为应用背景,进行了系统化的实验研 究,结果表明这一智能化的预测系统的性能是令人满意的.     

带有迭代学习外环的快速路入口匝道无模型自适应预测控制

针对快速路交通系统复杂时变以及难以建模的特点,首先,本文设计了基于无模型自适应预测控制的快速路入口匝道控制方案.其次,根据快速路交通系统具有重复性特点,本文在无模型自适应预测控制方法的基础上引入开环迭代学习控制,提出一种带有迭代学习前馈外环的无模型自适应入口匝道预测控制方案.相比无模型自适应预测控制方案,该方案可以利用迭代学习前馈控制器补偿系统可重复扰动,实现系统的完全跟踪.值得说明的是,预测控制器和学习控制器可以独立工作也可以联合工作.最后,文章给出了控制方案的收敛性分析,并通过交通流仿真验证了所提控制方案的有效性.     

基于神经网络与多模型的非线性自适应广义预测控制

针对一类不确定非线性离散时间动态系统, 提出了基于神经网络与多模型的非线性广义预测自适应控制方法. 该自适应控制方法由线性鲁棒广义预测自适应控制器, 神经网络非线性广义预测自适应控制器和切换机制三部分构成. 线性鲁棒广义预测自适应控制器保证闭环系统的输入输出信号有界, 神经网络非线性广义预测自适应控制器能够改善系统的性能. 切换策略通过对上述两种控制器的切换, 保证系统稳定的同时, 改善系统性能. 给出了所提自适应方法的稳定性和收敛性分析. 最后通过仿真实例验证了所提方法的有效性.     

一种基于人工神经网络在线学习的自适应预测方法

本文在分析和比较了传统预测方法的基础上，研究基于人工神经网络非线性映射的预测方法，针对一类缓变故障的预测问题，提出单样本在线学习的自适应预测算法，并用于柴油机故障预测和诊断。     

一种模糊神经网络自适应预测控制方案的研究

提出了一种模糊神经网络自适应预测控制方案,对学习公式进行了理论推导,并结合误差补偿以提高预测控制的精度。仿真实验表明,该算法可实现模糊控制和神经网络的优势互补,对非线性复杂系统具备良好的控制性能     

基于遗传算法的神经网络在销售决策中的应用

在传统的ERP中，销售管理的预测分析是一个难题。通过采用遗传算法采样得到输入值，通过BP神经网络进行复杂的非线性预测，以使ERP系统智能化，自动化水平更高，通过神经网络自适应学习和训练，找出输入和输出的关系，以求解问题。     

自适应模糊系统理论在负荷预测中的应用研究

应用自适应模糊系统理论的最新成果实现实用化的预测系统,并以电力电负荷预测为具体应用背景完成了实验研究,系统的建立和运行分别依赖于对历史数据和对实时数据的离线和在线学习,具有明显的自适应性和鲁棒性,通过合理的设计实现简洁的系统结构,通过在线训练确定优化的系统设置,短期负荷预测的日均相对误差小于２％,可以满足现场的实用化要求。     

基于MATLAB的线性神经网络自适应预测的实现

设计了一个线性神经网络 ,并对其系统进行自适应预测。通过利用 MATL AB6 .5中的神经网络函数adapt对线性网络的自适应训练 ,网络能够随着被预测的模型的变化而相应地对网络的权值和阈值进行修正 ,从而实现对它的自适应预测     

基于神经网络Smith预估器的预测控制

针对非线性系统时滞问题,给出了一种新型的单神经元Smith预测控制算法.神经网络的预测控制器由不完全微分的单神经元自适应PID控制器和神经网络的Smith预估器组成.预估器对输出进行多步预测,控制器超前动作以消除时滞对系统的影响.不完全微分的单神经元自适应PID控制器通过改进的Hebb学习规则实现其权值调节,通过权系数的在线调整实现自适应控制.仿真实验证明了该方法具有较快的响应速度和较好的响应性能.     

An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference

An expert system for used cars price forecasting using adaptive neuro-fuzzy inference system (ANFIS) is presented in this paper. The proposed system consists of three parts: data acquisition system, price forecasting algorithm and performance analysis. The effective factors in the present system for price forecasting are simply assumed as the mark of the car, manufacturing year and engine style. Further, the equipment of the car is considered to raise the performance of price forecasting. In price forecasting, to verify the effect of the proposed ANFIS, a conventional artificial neural network (ANN) with back-propagation (BP) network is compared with proposed ANFIS for price forecast because of its adaptive learning capability. The ANFIS includes both fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental result pointed out that the proposed expert system using ANFIS has more possibilities in used car price forecasting.     

一种改进的用于城市主干道行驶时间短时预测的自适应指数平滑(IAES)模型

短期行程时间预测对于智能交通系统来说至关重要. 本文首先回顾了交通短期预测模型研究现状并指出了它们的基本思想, 研究工作进展以及各种模型的优点和缺点. 为了克服原有的自适用指数平滑模型的缺点, 本文提出了一种改进的自适应指数平滑模型, 针对四条主干道车牌数据匹配数据, 对各种预测模型进行了正常交通状况和非正常交通状况的短期预测比较实验, 实验结果表明每一种模型都有优点和缺点, 而改进的自适应指数平滑模型的预测性能在短期行程时间预测方面表现了优于其它模型的独特特点, 并且能适用于各种交通状况．     

降水量的自适应神经网络模糊推理预报

为了对降水量进行建模与预测 ,介绍了自适应神经网络模糊推理系统 ,设计了基于神经网络的自适应模糊控制器 ,该网络能从一组操作数据中提取模糊控制规则 ,提高降水量预报的准确度。仿真结果表明 ,该方法是非常有效的。     

The study and application of a novel hybrid system for air quality early-warning

Air quality early-warning plays a vital role in improving air quality and human health, especially multi-step ahead air quality early-warning, which is significant for both citizens and environmental protection departments. However, most previous studies have only employed simple data decomposition to perform one-step forecasting and were aimed at enhancing forecasting accuracy or stability. Little research has improved these two standards simultaneously, leading to poor forecasting performance. Because of its significance, relevant research focused on multi-step ahead air quality early-warning is especially needed. Therefore, in this paper, a novel hybrid air quality early-warning system, which consists of four modules: data preprocessing module, optimization module, forecasting module and evaluation module, is proposed to perform multi-step ahead air quality early-warning. In this system, an effective data decomposition method called the modified complete ensemble empirical mode decomposition with adaptive noise is developed to effectively extract the characteristics of air quality data and to further improve the forecasting performance. Moreover, the hybrid Elman neural network model, optimized by the multi-objective salp swarm algorithm, is successfully developed in the forecasting module and simultaneously achieves high forecasting accuracy and stability. In addition, the evaluation module is designed to conduct a reasonable and scientific evaluation for this system. Three cities in China are employed to test the effectiveness of the proposed early-warning system, and the results reveal that the proposed early-warning system has superior ability in both accuracy and stability than other benchmark models and can be used as a reliable tool for multi-step ahead air quality early-warning.     

An evolutionary‐based adaptive neuro‐fuzzy inference system for intelligent short‐term load forecasting

The continuing growth in size and complexity of electric power systems requires the development of applicable load forecasting models to estimate the future electrical energy demands accurately. This paper presents a novel load forecasting approach called genetic‐based adaptive neuro‐fuzzy inference system (GBANFIS) to construct short‐term load forecasting expert systems and controllers. At the first stage, all records of data are searched by a novel genetic algorithm (GA) to find the most suitable feature of inputs to construct the model. Then, determined inputs are fed into the adaptive neuro‐fuzzy inference system to evolve the initial knowledge‐base of the expert system. Finally, the initial knowledge‐base is searched by another robust GA to induce a better cooperation among the rules by rule weight derivation and rule selection mechanisms. We show the superiority and applicability of our approach by applying it to the Iranian monthly electrical energy demand problem and comparing it with the most frequently adopted approaches in this field. Results indicate that GBANFIS outperforms its rival approaches and is a promising tool for dealing with short‐term load forecasting problems.     

多模型自适应预报及其在电力规划负荷预报中的应用

本文提出一种多模型自适应预报方法,预报系统由几个并行的多步预报器组成,最终预报由Bayes决策律决定。本法适用于随机快时变参数动态过程的预报,在我国工业用电量长期预报中应用此法获得了良好的效果。     

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.     

ANNSTLF-a neural-network-based electric load forecasting system

A key component of the daily operation and planning activities of an electric utility is short-term load forecasting, i.e., the prediction of hourly loads (demand) for the next hour to several days out. The accuracy of such forecasts has significant economic impact for the utility. This paper describes a load forecasting system known as ANNSTLF (artificial neural-network short-term load forecaster) which has received wide acceptance by the electric utility industry and presently is being used by 32 utilities across the USA and Canada. ANNSTLF can consider the effect of temperature and relative humidity on the load. Besides its load forecasting engine, ANNSTLF contains forecasters that can generate the hourly temperature and relative humidity forecasts needed by the system. ANNSTLF is based on a multiple ANN strategy that captures various trends in the data. Both the first and the second generation of the load forecasting engine are discussed and compared. The building block of the forecasters is a multilayer perceptron trained with the error backpropagation learning rule. An adaptive scheme is employed to adjust the ANN weights during online forecasting. The forecasting models are site independent and only the number of hidden layer nodes of ANN's need to be adjusted for a new database. The results of testing the system on data from ten different utilities are reported.