高柔结构风振AMD控制模糊神经网络预测算法研究

基于Takagi-Sugeno模型的模糊神经网络的基本理论,建立了模糊神经网络预测模型。该模型结合神经网络控制和预测控制的优点,解决了控制中的时滞问题。研究了基于聚类法产生模糊神经网络预测控制的模糊逻辑系统,该方法便捷地解决了模糊逻辑控制中模糊控制规则基于专家控制策略和经验而无自学习能力的困难。以深圳京基金融中心为算例,利用模糊神经网络预测算法控制结构在风荷载作用下的振动,仿真结果表明,模糊神经网络预测算法能够有效地减小高柔结构加速度响应。

神经网络预测算法在抽油机节能电机系统中的应用

抽油机在工作时,每台电机的实际负载都是不相同的,如何根据工作过程中抽油机的实际负载来调整电机的实际输入电压对提高抽油机的效率有着明显的提升。本文采用了神经网络预测算法对电机实际输入电压进行预测,讨论了神经网络预测算法参数对预测结果的影响,并对神经网络预测算法获得的预测值与实测值进行了对比。结果表明本文提出的神经网络预测算法能够对电机最佳工作电压进行预测。

模糊神经网络预测算法在高柔结构风振AMD控制中的应用研究

基于Takagi-Sugeno模型的模糊神经网络的基本理论，本文建立了模糊神经网络预测模型。该模型结合神经网络控制和预测控制的优点，解决了控制中的时滞问题。研究了基于聚类法产生模糊神经网络预测控制的模糊逻辑系统，该方法便捷地解决了模糊逻辑控制中模糊控制规则基于专家控制策略和经验而无自学习能力的困难。以深圳京基金融中心为算例，利用模糊神经网络预测算法控制结构在风荷载作用下的振动，仿真结果表明模糊神经网络预测算法能够有效地减小高柔结构加速度响应。

基于NARX神经网络预测及模糊控制的互联电网CPS鲁棒控制策略研究

对于传统CPS控制策略难以满足互联电力系统对鲁棒性和适应性的要求,提出了一种将NARX神经网络预测算法和模糊逻辑控制器相结合的控制方法。配合CPS下的传统PI控制器,根据CPS控制参数的预测值与当前值之间的偏差值,实现对AGC机组的预控制。利用Matlab的Simulink仿真软件建立了一个双区域电力系统的控制模型。仿真结果表明,新的控制方法不仅达到了改善CPS控制效果的目的,并且提高了CPS1,CPS2指标的考核率,减少了机组的调节次数,降低了运行费用,取得了一定的经济效益。

面向轻汽油醚化的BP神经网络的模型预测控制

针对催化裂化轻汽油(Fcc轻汽油)醚化的过程提出了BP神经网络的模型预测控制,通过控制Fcc轻汽油的流速,来实现重油量浓度指标的控制。应用BP神经网络建立该过程的预测模型,并采用迭代优化的控制算法,根据相应的性能指标,不断地修正神经网络的权值,从而整定下一批次的控制信号。通过Matlab里的神经网络工具箱,建立一个有参考模型的神经网络预测控制器,观测最终的实际输出。

基于VMPSO-RBF神经网络的话务量预测

为了更快速、准确地预测话务量,提出了速度变异的粒子群算法(VMPSO),并与RBF算法相结合,形成速度变异的粒子群—RBF(VMPSO-RBF)神经网络算法,并且来训练神经网络,从而优化了神经网络的参数,最后对移动话务量进行预测。与RBF神经网络方法和PSO-RBF神经网络方法相比较,该文提出的方法预测精度更高,收敛速度更快。

基于时间序列分析的航站楼安检旅客流量预测

对单位时间内通过安检的旅客流量进行预测是机场航站楼实时调控的重要依据,由此提出一种实时安检旅客流量预测方法,采用Wolf方法分析出安检旅客流量时间序列具有混沌特性;采用适用于混沌时间序列预测的遗传算法优化BP神经网络预测方法(GABP)预测安检旅客流量;分别设定时间尺度为2 min、5 min和10 min等,分析不同时间尺度对安检旅客流量预测精度的影响。基于北京首都国际机场T3航站楼实际安检旅客流量数据的实验结果表明,采用GABP神经网络对以2 min为时间尺度的安检旅客流量预测能取得较好的预测精准度。

隧道洞内外智能交通协同管控应用探究

考虑到北京市石门路节点处发生严重交通拥堵时,路口排队车辆会蔓延至隧道洞口,大大增加隧道洞内外发生交通事故的概率。因此,以交通流量采集作为检测手段,以神经网络预测控制算法作为控制策略,信息通过传输、分析、反馈,利用信号控制机实现隧道外洞口交通信号灯合理配时,同时在隧道交通诱导及信息发布屏上及时显示并预警路况等信息。最终实现交通流的有效诱导以及隧道洞内外的智能交通协同管控,为工程设计提供了参考和实践路径。

基于云平台的室内环境舒适度智能决策控制系统

针对目前室内环境实时数据采集与监测系统缺乏环境舒适度智能决策控制功能,提出了基于云平台的室内环境舒适度智能决策系统设计方案。使用ESP32单片机作为终端数据采集和控制核心;使用消息队列遥测传输(message queuing telemetry transport,MQTT)作为终端与云平台数据传输协议;使用阿里云物联网平台作为终端设备管理和服务平台;使用云平台的RRPC(MQTT同步通信)协议同步至自建阿里云公网服务器;在自建服务器使用智能模糊决策模型结合BP(back propagation)神经网络模型构建室内环境舒适度的智能决策控制算法。实验结果表明,该系统能精确地实现环境舒适度智能决策控制功能。

STUDY ON ARTIFICIAL NEURAL NETWORK FORECASTING METHOD OF WATER CONSUMPTION PER HOUR

An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.

物流园区物流量处理能力分析与计算

物流园区是近年来我国现代物流业发展中出现的新型态,对于物流园区物流量处理能力的估算一直是现在很多学者研究的焦点。现在计算处理能力的主要方法是通过参数标定计算各物流模块处理物流量后再叠加,但现有研究均未尝试通过人工神经网络法拟合物流园区各功能模块的面积与总物流量处理能力的关系,再通过两者关系进行物流量处理能力测算。

NEURAL NETWORK PREDICTIVE CONTROL WITH HIERARCHICAL GENETIC ALGORITHM

A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.

Optimizing neural network forecast by immune algorithm

Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.

Research on Prediction of Red Tide Based on Fuzzy Neural Network

In this paper, a four-layer fuzzy neural network using the Back Propagation （BP） Algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.

Development of viscosity model for aluminum alloys using BP neural network

Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model.Back-propagation(BP)neural network method was adopted,with the melt temperature and mass contents of Al,Si,Fe,Cu,Mn,Mg and Zn solutes as the model input,and the viscosity value as the model output.To improve the model accuracy,the influence of different training algorithms and the number of hidden neurons was studied.The initial weight and bias values were also optimized using genetic algorithm,which considerably improve the model accuracy.The average relative error between the predicted and experimental data is less than 5%,confirming that the optimal model has high prediction accuracy and reliability.The predictions by our model for temperature-and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature,indicating that the developed model has a good prediction accuracy.

Prediction of 2A70 aluminum alloy flow stress based on BP artificial neural network

The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network （ANN） model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ±3% for the sampled data while it was less than ±6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.

STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency for the network to transform to an issue of local solution, a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP, that is, the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights, trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

Airport Aviation Noise Prediction Based on an Optimized Neural Network

In order to alleviate noise pollution and improve the sustainability of airport operation,it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is constructed to gain computational simplicity and execution economy. With the preferred node number and transfer functions obtained in comparative tests,the constructed network is further optimized through the genetic algorithm for performance improvements in prediction. Results show that the proposed model in this paper is superior in accuracy and stability for airport aviation noise prediction,contributing to the assessment of future environmental impact and further improvement of operational sustainability for civil airports.

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.

Times Series Prediction to Basis of a Neural Network Conceived by a Real Genetic Algorithm

Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.