A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller （FNNC）, which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the filzzy logic control algorithm.     

基于局部泛化误差模型的RBFNN的启发式训练方法

研究如何应用吴永贤(W.W.Y.NG)提出的局部泛化误差模型来训练径向基函数神经网络(RBFNN),给出了一种训练RBFNN的启发式训练方法.实验表明,该方法成功解决了模型结果计算时间复杂度问题,同时RBF'NN的训练精度也达到令人满意的结果.     

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller (NFC) in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.     

Calculation method of ship collision force on bridge using artificial neural network

Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method （FEM） which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network （ANN）. The radial basis function neural network （RBFNN） employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation effi- ciency in application using MATLAB software.     

基于径向基神经网络的车牌字符识别算法研究

根据车牌中字符的形态特征和横向纹理属性,以小波空间作为车牌字符识别的特征空间,提取字符的小波统计特征作为RBF神经网络的输入进行字符识别。实验结果表明本文提出的算法是一种切实可行、准确高效的方法,对复杂背景下拍摄的汽车牌照识别具有很好的鲁棒性。     

基于模糊观测数据的RBF神经网络回归模型

提出了一种基于模糊观测数据的RBF神经网络（FORBFNN）,用于解决一类输出不可精确测量但可用模糊隶属度来表征的非线性系统建模问题．神经网络模型中各隐层神经单元的权重系数采用一种新的模糊EM算法辨识获得;隐层神经单元的数量及径向基函数的中心和宽度基于一种数据驱动的方法自适应确定,即首先初始生成一个隐层单元,然后根据一定的规则逐步加入新的单元,该过程不断迭代直到模型满足预设要求．该方法同时考虑了模型的复杂度及预测精度．数值模拟实验结果表明该建模方法是有效的,且建立的模型具有较高的预测精度．     

基于RBFNN实验的物业税开征后对房产价格影响预测

本文建立径向基神经网络(Radical Basis Function Neural Network-RBFNN)遥近微观经济学理论的DUST价格函数,分析了物业税开征对于上海住房价格的影响效应.实证结果显示,流通环节的税收改革对房价产生的是持久的正向冲击,保有环节的税收改革对房价产生的是短期的负向冲击.在基于实验结论的基础土,给出了相应的经济学分析,并提出了相应的政策建议.     

基于局部泛化误差模型的RBFNN的启发式训练方法

研究如何应用吴永贤（W.W.Y.NG）提出的局部泛化误差模型来训练径向基函数神经网络（RBFNN）,给出了一种训练RBFNN的启发式训练方法.实验表明,该方法成功解决了模型结果计算时间复杂度问题,同时RBFNN的训练精度也达到令人满意的结果.