Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Application of hybrid coded genetic algorithm in fuzzy neural network controller

Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.

Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

Transient Air-Fuel Ratio Control in a CNG Engine Using Fuzzy Neural Networks

The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.

A Hybrid Compensation Scheme for the Input Rate-Dependent Hysteresis of the Piezoelectric Ceramic Actuators

A hybrid compensation scheme for piezoelectric ceramic actuators(PEAs)is proposed.In the hybrid compensation scheme,the input rate-dependent hysteresis characteristics of the PEAs are compensated.The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model,while the feedback controller is a proportion integration differentiation(PID)controller.In the proposed inverse model,an input ratedependent auxiliary inverse operator(RAIO)and output of the hysteresis construct the expanded input space(EIS)of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping,and the wiping-out,rate-dependent and continuous properties of the RAIO are analyzed in theories.Based on the EIS method,a hysteresis neural network inverse model,namely the dynamic back propagation neural network(DBPNN)model,is established.Moreover,a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis.Finally,the proposed method,the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii(MRPI)model are all applied in the experimental platform.Experimental results show that the proposed method has obvious superiorities in the performance of the system.

Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals

A novel separation identification strategy for the neural fuzzy Wiener–Hammerstein system using hybrid signals is developed in this study.The Wiener–Hammerstein system is described by a model consisting of two linear dynamic elements with a nonlinear static element in between.The static nonlinear element is modeled by a neural fuzzy network(NFN)and the two linear dynamic elements are modeled by an autoregressive exogenous(ARX)model and an autoregressive(AR)model,separately.When the system input is Gaussian signals,the correlation technique is used to decouple the identification of the two linear dynamic elements from the nonlinear element.First,based on the input and output of Gaussian signals,the correlation analysis technique is used to identify the input linear element and output linear element,which addresses the problem that the intermediate variable information cannot be measured in the identified Wiener–Hammerstein system.Then,a zero-pole match method is adopted to separate the parameters of the two linear elements.Furthermore,the recursive least-squares technique is used to identify the nonlinear element based on the input and output of random signals,which avoids the impact of output noise.The feasibility of the presented identification technique is demonstrated by an illustrative simulation example and a practical nonlinear process.Simulation results show that the proposed strategy can obtain higher identification precision than existing identification algorithms.

T-S norm FNN controller based on hybrid learning algorithm

Aiming at the problems that fuzzy neural network controller has heavy computation and lag,a T-S norm Fuzzy Neural Network Control based on hybrid learning algorithm was proposed.Immune genetic algorithm (IGA) was used to optimize the parameters of membership functions (MFs) off line,and the neural network was used to adjust the parameters of MFs on line to enhance the response of the controller.Moreover,the latter network was used to adjust the fuzzy rules automatically to reduce the computation of the neural network and improve the robustness and adaptability of the controller,so that the controller can work well ever when the underwater vehicle works in hostile ocean environment.Finally,experiments were carried on " XX" mini autonomous underwater vehicle (min-AUV) in tank.The results showed that this controller has great improvement in response and overshoot,compared with the traditional controllers.

A Hybrid TCNN Optimization Approach for the Capacity Vehicle Routing Problem

A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP.

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.

基于卷积门控循环单元的波浪发电系统输出功率预测

为高效准确预测波浪输出功率,提出卷积神经网络和门控循环单元混合模型波浪预测算法。采用间接预测方法,搭建直驱式波浪发电系统模型,运用CORREL函数分析不同波浪特征的相关性,结合卷积神经网络提取特征与高维空间中的波高关系,构造特征向量,通过门控循环单元网络进行训练,将全连接层的输出值经反归一化后获得预测波高值,输入所搭建模型,获得波浪输出功率预测值。仿真结果表明,与其他网络模型相比,在多特征输入情况下,混合模型波浪预测算法预测效率更高、精度更准确。

基于CNN-LSTM网络的交直流电网故障线路识别方法

针对交直流混联电网故障特征复杂、故障线路识别率低的局限性,提出一种基于模糊熵(FuzzyEn)结合改进的卷积神经-长短期记忆网络(CNN-LSTM)的交直流电网故障线路识别方法。首先使用改进的小波阈值滤波算法对数据进行降噪处理,再利用模糊熵提取信号的故障特征。接下来在卷积神经网络(CNN)的全连接层上,添加一层长短期记忆网络(LSTM),对模糊熵提取的故障特征进行分类,最终实现故障线路的准确识别。实验结果表明,该算法识别准确率达到了99.5%,能够有效地诊断出交直流混联电网故障线路,同时在10 dB噪声干扰下仍达到96.0%的识别准确率,具有较好的抗噪声能力。

从神经网络中抽取土地评价模糊规则

为了明确土地评价中所训练神经网络的含义,使土地评价工作者可轻松地理解、判断所得到土地评价模型的正确性和合理性,提出从神经网络中抽取土地评价模糊规则的方法。现有的大多数从神经网络中提取方法,神经网络的输入属性要么局限于连续的,要么只适应于离散的,而土地评价因子往往既包含连续的又包含离散的、标称的,该文首先提出了一种输入属性值适应于这三种类型数据的模糊神经网络建立方法,进而给出一种从建立的神经网络中抽取其中较主要模糊规则的算法。试验表明,所提出的土地评价方法,可直接从样本中学习评价规律,使土地评价工作者易于理解,当出现抽取的规则与实际情况不吻合时,可重新训练神经网络和抽取规则,所得到的评价结果比BP网络的评价结果更准确,从而提高了土地评价的准确性。

基于神经网络的混合动力汽车驾驶意图识别方法"

建立了基于Takagi-Sugeno模型的模糊神经网络。通过对模糊神经网络进行训练,生成了驾驶意图模糊推理规则。从仿真结果可以看出运用本方法得到的模糊推理规则可以很好地识别驾驶意图,并且基于驾驶意图识别可以有效地优化混合动力汽车的控制策略,从而进一步提高混合动力汽车燃油经济性。

神经网络工况识别的混合动力电动汽车模糊控制策略

采用模糊控制可以改进混合动力电动汽车(HEV)的燃油经济性和排放性,但是对模糊控制器进行优化时通常只针对某一典型工况.不同的城市的行驶工况有一定差别,影响了模糊控制改善混合动力电动汽车性能的效果.研究中以广州和上海市主干道行驶工况为例,首先建立了一个模糊控制策略,并采用遗传算法,以汽车燃油经济性和排放性为优化目标,分别针对广州和上海主干道行驶工况对模糊控制器中隶属度函数进行优化.然后建立了一个基于模糊神经网络的行驶工况识别方法,通过识别广州和上海的主干道行驶工况,对控制策略中模糊控制器的隶属度参数进行相应调整,结果证明采用模糊神经网络识别行驶工况的HEV模糊控制策略可以进一步提高汽车的燃油经济性和排放性能.

基于熵聚类模糊神经网络味觉信号识别系统的研究

提出了一种基于熵聚类的模糊神经网络味觉信号识别系统模型 ,该模型利用聚类方法实现模糊输入空间划分和模糊IF THEN规则提取 ,并使用梯度下降法对系统参数进行精炼 系统兼具有良好的可解释性和学习能力 ,对 1

基于Zigbee通信的节能型混合式机械增氧系统

构建了一套增氧系统,采用耕水机和微孔曝气增氧机混合增氧的模式,白天通过定时控制以耕水机工作为主,晚上或阴雨天缺氧时以微孔曝气增氧为主。在环境参数不断变化的情况下,为了保持溶解氧的稳定,控制方法采用误差反传的模糊神经网络控制。试验表明,在相同条件下采用混合式增氧控制较传统的叶轮式增氧可节约电能40.6%,提高产量31.9%,最终利润提高136.1%。水质参数测量采用Zigbee通信,通信协议采用优化的低能量自适应分层协议,并根据水体溶解氧测量的实际要求,设置参数测量的软、硬阈值以减少节点数据发送的次数,达到节能和供电电池剩余能量均衡的目的,试验表明优化后的无线传感网络寿命延长了58%。

混合智能系统研究综述

混合智能系统作为人工智能研究的一个新兴领域已受到人们越来越多的关注.首先,回顾了混合智能系统的发展历程,并以此为基础重新给出了混合智能系统的定义.接着,从理论和实际应用两个角度对混合智能系统研究现状进行了综述.从理论研究上看,主要综述了混合智能系统的研究动因、类别、构造方法、评价准则,对当前研究现状进行了分析;从应用研究上看,主要对混合智能系统的主要应用领域进行了综述.最后,以目前混合智能系统领域的研究论文为基础,对混合智能系统未来的发展方向进行了预测.

遗传算法及模糊、神经网络融合技术的研究

介绍了遗传算法与神经网络、遗传算法与模糊逻辑系统的融合方式及结构，并通过遗传算法、模糊、神经网络三者的融合技术对倒立摆进行控制，介绍了参数优化方法，说明了这种融合技术的可行性、实用性和通用性。

基于模糊粗糙集和神经网络的短期负荷预测方法

针对采用神经网络进行电力系统短期负荷预测时其网络输入变量的选择是影响预测效果的关键问题,该文提出使用模糊粗糙集理论解决这一问题:对采集到的信息进行特征提取、形成决策表:利用模糊粗糙集理论进行属性约简、去除冗余信息:用得到的属性作为BP网络的输入进行训练预测。该方法既全面考虑了影响负荷预测的历史时间序列、气象等各种因素,为合理地选择神经网络的输入变量提供了一种新的方法,又避免了由于输入变量过多而导致神经网络拓扑结构复杂、训练时间长等不足。计算实例表明,文中提出的方法是有效且可行的。