Grey prediction on indoor comfort temperature for HVAC systems

This paper describes determination of indoor comfort temperature for efficiently Heating, Ventilating, and Air-Conditioning (HVAC) system under dynamical environment. Making occupants’ satisfaction on thermal comfort is still challenging by how the temperature setpoint of the fresh made-up air in conventional HVAC systems can be adapted properly (normally fixed) when surroundings changes in time. Essentially, being unknown ahead of time, the outdoor temperature is systematically predicted by grey prediction model in this work. The Adaptive Comfort Theory (ACT) model captures relation of the indoor comfort temperature to the outdoor temperature based on the survey data on thermal comfort in real occupants’ living environment. With the grey prediction model and the ACT model, the predicted indoor comfort temperature can be implemented as the comfort temperature reference for HVAC control systems. The experiment results show the viability of the proposed methodology for efficient HVAC control system.     

面向高铁站的热舒适度和能耗综合预测

针对高铁站这类半封闭建筑的热舒适度影响因素众多,影响机制复杂以及热舒适度与能耗存在背反等问题,提出了基于机器学习的高铁站热舒适度与能耗综合预测方法。首先采用传感器数据捕获及Energy Plus仿真两种方式对高铁站室内外状态、多联机及热交换机等控制单元及热能传导环境进行建模;其次提出影响高铁站热舒适度的八类因素——多联机开启台数、多联机设置温度、热交换机开启台数、客流密度、室外温度、室内温度、室内湿度和室内二氧化碳浓度,并设计424种模型运行工况以及3 714 240个实例;最后设计6种机器学习模型——深度神经网络、支持向量回归、决策树回归、线性回归、岭回归和贝叶斯岭回归,来对高铁站室内热舒适度和空调能耗进行有效预测。实验结果表明,6种机器学习模型中决策树回归预测模型能够在较短的时间内获得最优的预测性能,其平均均方误差低至0.002 2。所得研究成果可直接为下一阶段的温控策略提供主动预判的环境状态参数并实现实时决策。     

Cooperative control of multiple neural networks for an indoor blimp robot

We report on the cooperative control of multiple neural networks for an indoor blimp robot. In our research group, the indoor blimp robot has been studied to achieve various flying robot applications. The objective of this article is to propose a robust controller that can adapt to mechanical accidents such as the breakdown of propellers. In our proposed method, each propeller thrust is independently calculated by a small neural network. We confirm the advantage of the proposed method against the control by a single large neural network. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Principal Feasability Studies Using Neuro – Numerics for Prediction of Flow Fields

In this paper initial studies of the application of a hybrid model using artificial neural networks and conventional numerical methods to predict – as an example – twodimensional, isothermal, steady flow fields is presented. Main topics of the work were to show the principal possibility of using ANN in fluid mechanics and, additionally, to realize the potential of incorporation a priori knowledge of physical phenomena into the training procedure. For training, as well as for rating the prediction, flow fields, consisting of velocity, temperature and pressure, were generated by numerical simulation. Major result was that prediction of the flow and especially the existance of vortices in the bodies outflow depending on the Reynolds number can be realized with a much lesser time consumption than necessary for numerical calculation. Furthermore, a priori physical knowledge could be included in the learning process with an obvious improvement of the predicting ability of the hybrid model.     

基于小波模糊神经网络的实时交通流预测

实时交通流预测是智能运输系统研究的重要内容之一.本文将小波分析的相关知识与模糊神经网络相结合,给出了基于小波模糊神经网络的交通流预测模型,采用小波函数作为模糊隶属度函数,用神经网络来实现模糊推理,完成对下一个周期性交通流的估计.同时,用遗传算法来优化整个网络,实测数据验证这种方法预测精度高,收敛过程平稳,适应性强.     

基于TinyOS的无线传感网络温湿度采集系统

无线传感器网络在智能家居系统中有广泛的应用前景,温度和湿度是影响室内舒适度的两个重要参数。提出了基于TinyOS的无线传感网络温湿度数据采集系统,设计了无线节点模块以及传感模块,分析了TinyOS的运行机制和开发步骤,开发了基于 TinyOS 的底层硬件驱动程序和上位机系统,组成了多跳网络。实验研究表明系统运行良好,达到了预期的设计要求。     

快速二阶BP网络及其在城市用水量预测中的应用

针对BP网络收敛速度慢，易导致局部极小值的缺点，提出一种快速二阶BP网络，并以城市年用水量预测为例，与BP网络对比，结果表明，该方法加快了收敛速度，提出了结果的准确度。     

基于改进遗传算法的RBF神经网络结构优化研究

针对RBF神经网络隐含层节点数过多导致网络结构复杂的问题,提出了一种基于改进遗传算法(IGA)的RBF神经网络优化算法。利用IGA优化基于正交最小二乘法的RBF神经网络结构,通过对隐含层输出矩阵的列向量进行全局寻优,从而设计出结构更优的基于IGA的RBF神经网络(IGA-RBF)。将IGA-RBF神经网络的学习算法应用于电子元器件贮存环境温湿度预测模型,与基于正交最小二乘法的RBF神经网络进行比较的结果表明:IGA-RBF神经网络设计出来的网络训练步数减少了44步,隐含层节点数减少了34个,且预测模型得到的温湿度误差较小,拟合精度大于0.95,具有更高的预测精度。     

神经网络在股市行情预测中的应用

人工神经网络以其独特的信息处理特点应用于预测领域具有很大的理论和实际应用价值。本文以股市行情为对象，详细论述了神经网络在股市行情预测领域中的应用，给出了相应的神经网络模型和两种历史数据的预处理方法，通过实验和测试，证明了这种方法的可行性和正确性，文章最后还对神经网络预测方法和基于数据统计基础之上的定理预测方法进行了比较。     

Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions

Three passive cooling methods (e.g. roof pond, reflective roof cooling and using insulation over the roof) have been experimentally evaluated using an experimental test structure. The objective of this work is to train an artificial neural network (ANN) to learn and predict the indoor temperature of room with the different experimental data. Different training algorithms (traingd, traingdm, traingdx, trainrp, traincgp, traincgf, traincgb, trainscg, trainbfg, trainoss, trainlm, and trainbr) were used to create an ANN model. This study is helpful in finding the thermal comfort of building by applying different passive cooling techniques. The data presented as input were outside temperature, relative humidity, solar intensity and wind speed. The network output was indoor temperature. The advantages of this approach are (i) the speed of calculation, (ii) the simplicity, (iii) adaptive learning from examples and thus gradually improve its performance, (iv) self-organization and (vi) real time operation. Results proved highly satisfactory and provided enough confidence for the process to be extended to a larger solution space for which there is uneconomical and time consuming way of calculating the solution.     

改进RPE算法的神经网络在客户欺诈预测中的应用

客户欺诈在一定程度上抑制了消费，这会妨碍电信运营商和电信用户的亲密度，从而削弱电信运营商的市场竞争力。客户欺诈现象存在非常复杂的多元非线性关系，从统计学角度出发，难以建立预测模型，针对这些问题，提出了基于递推预报误差(RPE)算法神经网络的方法建模，并采用改进的动态遗忘因子方法保证了平稳收敛。实验结果表明，用该算法预测客户欺诈的危险度效果优于BP神经网络模型，具有实用性和有效性。     

Models for Prediction of Physiological Responses of Holstein Dairy Cows

The goal of the present study was to evaluate techniques for modeling the physiological responses, rectal temperature, and respiratory rate of black and white Holstein dairy cows. Data from the literature (792 data points) and obtained experimentally (5884 data points) were used to fit and validate the models. Each datum included dry bulb air temperature, relative humidity, rectal temperature, and respiratory rate. Two models based on artificial intelligence—artificial neural networks and neurofuzzy networks—and one based on regression were evaluated for each response variable. The adjusted models predict rectal temperature and respiratory rate as a function of dry-bulb air temperature and relative humidity. These models were compared using statistical indices. The model based on artificial neural networks showed the best performance, followed by the models based on neurofuzzy networks and regression; the last two performed similarly.     

基于构造性神经网络的时间序列混合预测模型 *

针对传统时间序列预测模型不适应非线性预测而适应非线性预测的BP算法存在收敛速度慢,且容易陷入局部极小等问题,提出一种基于构造性神经网络的时间序列混合预测模型。采用构造性神经网络模型（覆盖算法）得出的类别值对统计时间序列模型的预测值进行修正,建立一种同时考虑时间序列自身周期变化和外生变量因子对时间序列未来变化趋势影响的混合预测模型,涵盖了实际问题的线性和非线性两方面,提高了预测精度。将该模型应用到粮食产量的预测中,取得了较好的预测效果。     

基于构造性神经网络的时间序列混合预测模型 *

针对传统时间序列预测模型不适应非线性预测而适应非线性预测的 BP算法存在收敛速度慢 ,且容易陷入局部极小等问题 ,提出一种基于构造性神经网络的时间序列混合预测模型。采用构造性神经网络模型 (覆盖算法 )得出的类别值对统计时间序列模型的预测值进行修正 ,建立一种同时考虑时间序列自身周期变化和外生变量因子对时间序列未来变化趋势影响的混合预测模型 ,涵盖了实际问题的线性和非线性两方面 ,提高了预测精度。将该模型应用到粮食产量的预测中 ,取得了较好的预测效果。     

Efficient forest fire occurrence prediction for developing countries using two weather parameters

Forest fire occurrence prediction plays a major role in resource allocation, mitigation and recovery efforts. This paper compares two artificial intelligence based methods, artificial neural networks (ANN) and support vector machines (SVM), utilizing a reduced set of weather parameters. Using a reduced set of parameters results in an efficient and reduced cost prediction system especially for developing countries. In this paper the aim is to predict forest fire occurrence by reducing the number of monitored features, and eliminating the need for weather prediction mechanisms. The reason is to reduce errors due to inaccuracies in weather prediction. The challenge is to choose a limited number of easily measurable features in the aim of reducing the cost of the system and its maintenance. At the same time, the chosen features must have a high correlation with the risk of fire occurrence. A literature review of forest fire prediction methods divided into systems/indices, and artificial intelligence is provided. The two fire danger prediction algorithms utilize relative humidity and cumulative precipitation to output a risk estimate. The assessment of these algorithms, using data from Lebanon, demonstrated their ability to accurately predict the risk of fire occurrence on a scale of four levels.     

基于自适应门控图神经网络的交通流预测

交通流预测是智能交通系统中的重要组成部分,由于交通数据的复杂性,长期而又准确的交通流预测一直是时间序列预测中最具挑战性的任务之一。近年来,研究人员将基于图神经网络的时空图建模方法应用于交通流预测任务,并取得了良好的预测性能。然而,现有的图建模方法仅通过预定义的邻接结构反映道路网络中的空间依赖关系,忽略了各节点之间的序列关联关系对预测的重要性。针对这一局限性,提出了一种自适应门控图神经网络（Ada-GGNN）,其核心为通过空间传递模块同时捕获道路网络的空间结构及自适应的时序相关性,并通过门控机制学习节点上的时间序列特征。在两个真实交通网络数据集PeMSD7和Los-loop上的实验结果证明了该模型具有更优越的性能。     

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.     

基于三次样条权函数神经网络的钢坯温度预报

针对传统神经网络对铜坯温度预报存在的一些缺点,如：隐层数不易确定．网络训练对初值敏感等。该文利用三次样条权函数神经网络建立了钢坯温度预报模型．克服了传统神经网络的缺点。仿真结果表明该模型具有较高的精度。     

基于混沌时间序列的变电站设备温度预测

变电站输变线路和设备的温度变化能够反映其老化、负载过高等引起的安全隐患.通过对变电站设备温度数据的非线性分析和预测,实现对设备的有效预警,将避免事故引起的巨大损失.对变电站已测温度数据建立时间序列,利用小数据量法验证变电站设备温度时间序列的混沌特性.研究基于RBF神经网络的混沌时间序列预测并与神经网络预测进行对比,单步预测与多步预测结果均优于神经网络预测.仿真结论证明了基于神经网络的混沌时间序列预测方法的有效性.     

Memetic evolutionary training for recurrent neural networks: an application to time-series prediction

Abstract: Artificial neural networks are bio-inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient-based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long-term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time-series prediction problems.