Multiple Layer BP Neural Network for Fuzzy Calculating

The FCBP(Fuzzy calculating BP) algorithm which is proposed by this paper hasovercome the sensitivity for samples,reduced the number of input layer's samples,lightened the burden of input layer.it is suitable to fuzzy inference and pattern recognition.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.     

The Application of Fuzzy Neural Networks for Power Transformer Fault Diagnosis

This paper presents the application of fuzzy neural networks (FNN) in power transformer faults diagnosis.A FNN model is builded,in which input is transformer oil color spectrum analysis and output is fault type.The test results of fault examples show its effectiveness and potential applicable worthiness.     

Improved Ellipsoidal unit Neural Networks and Its Applications in CSTR

To overcome the limitations of the standard ellipsoidal unit neural networks, some new approaches used in ellipsoidal unit neural networks have been proposed. These new approaches address three main issues: firstly, to understand better and represent the nature of fault classification boundaries; secondly, to determine the network structure without the usual trial and error schemes; lastly, to avoid erroneous generalizations. The application in CSTR shows that the ellipsoidal unit networks can possess arbitrary nonlinear classifying ability, nonlinear interfacial describing ability, and obtain accurate and efficient diagnosis results.     

Fuzzy Clustering Theory for Analyzing Intrusion Detection Data

Intrusion detection system is an important component of the computer and information security framework. Its main goal is to differentiate between normal activities of the system and behaviors that can be classified as suspicious or intrusive, and its main challenge is to efficiently detect intrusion detection behaviors for reducing false positive rate and false negative rate. In view of the disadvantages of the existing intrusion detection methods, fuzzy c-means(FCM) clustering method is used to analyze intrusion detection data in order to detect anomaly network behavior patterns. Experimental results on the CUP99 data set data show that this method can not only feasible but also improve the accuracy and efficiency.     

The Fuzzy Inputing And Outputing Method in Vibration Fault Diagnosis of Steam Turbine-Generator Set

A fuzzy neural network diagnosis model is established on the basis of the vibration failure features of steam turbine-gernerator set, two kinds of fuzzy inputing method are discussed. At last, the performance of the fuzzy neural network is compared with that of the conventional BP network. The results show that the method presented is suitable for identifying the vibration failure of steam turbine-gernerator set, and it is more efficient in deal with the uncertain data than BP network diagnosis.     

The Study on the Branched Feedforward Neural Network

Based on the BP algorithm,the Branched Feedforward Neural Network,one ofthe artificial neural network medel used for pattern classification is proposed in this paper, and therelated algorithm is also offered here, Several typical examples of pattern classification are studiedwith computer simulation, The simulation results show that the training time of Branched Feedforward Neural Network is obviously reduced and the classifying effect is much better as compared withgeneral BP Network.     

The Software Implementation of Neural Network Fuzzy Inference Cooperation System

This poper sets up a new Neural Network Fuzzy inference Cooperation system which is based on fuzzy inference principle in Expert Systems,take advantage of the powerful learning function, the little sensitivity to input layer's samples,and the fast convergence and so on in FCBP and take advantage of the good sort property and good suit property to fuzzy value in MFART Network.     

Design and Application of Compensatory Fuzzy Neural Network Based on Adaptive Learning Rate Method

To overcome the disadvantages of general networks such as slow convergence speed and being unable to combine with the expert knowledge etc,the authors introduces compensatory fuzzy neural cells,integrate the powerful knowledge expressiveness of fuzzy system and the excellent self-learning of neural network,and present a novel Compensatory Fuzzy Neural Network(CFNN) based on Adaptive Learning Rate Method which changes the learning rate using Adaptive Learning Rate Method in dynamic way.Finally this method is applied to the actual case.The result proves that it not(only) can adjust parameters properly on line,but also can optimize relevant fuzzy reasoning in dynamic way,fasten training rate.     

遗传算法优化人工神经网络模型在日光温室湿度预报中的应用

提出一种基于遗传算法优化BP神经网络的方法预测日光温室湿度环境因子。实测日光温室内影响空气湿度的环境因子组成数据样本作为神经网络的输入,采用基于实数编码的遗传算法替代随机设定神经网络的初始权阈值,然后通过改进的BP算法在由遗传算法确定的搜索空间中对网络进行精确训练。模型预报值和实测值基于1：1线的决定系数R2和预测平均相对误差MSE分别为0.9857和3．1％。结果表明,遗传算法优化BP神经网络预报模型收敛速度快、预测精度高。可为日光温室的湿度环境调控制提供理论依据和决策支持。     

Fault Detection Techniques for Feeder Automation

Fault detection of the distribution systems is one of the key techniques for feeder automation. At all times, it is still very difficult for the exact detection of single phase to ground fault. First of all, the distinguishing rules of the faulted phase are presented when ground fault occurs in distribution systems. This paper analyzes the fault detection technique for single phase to ground in system with floating neutral point. Based on faulted phase's current, a method realizing ground fault protection is proposed and the algorithm model of action criterion is given out. The simulation results show that the method will be with very good selectivity to ground fault, and it can be used preferably in feeder automation. The detection method for short circuit fault is also introduced simply.     

用模糊神经网络提高洪峰预报精度的研究

在大量研究的基础上,提出了基于模糊理论的神经网络改进算法,用来提高对洪峰的预报精度。该方法在网络训练时引入模糊理论来确定网络误差修改的程度。引入的算法增大了大值输出样本和期望输出的误差,使得网络向着提高洪峰拟合精度的方向修改权重。应用表明,改进的模糊BP神经网络能够较好的反映洪水演进机理,提高了神经网络洪水预报模型对洪峰的预报精度,保证了洪峰预报的可靠性。     

The Neural Network Method for Searching Graph Minimal Cut Set

The new neural network algorithm for searching graph minimal cut set,called NNMC algorithm, is established on the basis of construction of Hopfield Network energy function according to the concrete problem.In NNMC algorithm, which suffciently uses the fast convergence of Hopfield Network, a way to avoid the local minimum is carried out.     

BP神经网络在2005年太湖蓝藻水华预警中的应用

蓝藻水华是目前中国乃至世界面临的重大环境问题之一。为了有效地减少及预防蓝藻水华带来的影响,收集苏州市吴县1986—2007年的气象资料,运用主成分分析法,分析了太湖蓝藻暴发前一个月的主要限制因子及其相互关系。气温、气压、相对湿度、降水是影响叶绿素a浓度的主要限制因子。结合2005年1—10月太湖各区域蓝藻叶绿素a浓度的含量,利用Matlab R2010a软件,建立了基于BP神经网络的蓝藻水华预警模型,可为采取相应措施和控制蓝藻水华提供科学依据。     

A Kind of Fuzzy System Modelling and Control Based on Neural Networks

Combining artificial neural networks(ANN) with fuzzy system theory,a kind of modelling & control method of fuzzy system based on ANN is presented.The simulation researches have verified that the proposed approach can be applied effectively to a number of control systems which are defficult to build strict mathematical model.     

The Research of Using BP Neural Network in Prediction of Woods Fire Insurance Grade

The relation between circumfluence index and woods fire.index is described. BP neural network is trained by the known circumfluence index and woods fire index. The restricted feedback and stimulant feed forwardcalculation are to realize self-organizational learningof BP Network.Thereby the nonlinear mapping between circumfluence index and woods fire.index isperformed. With this network, characteristic choice can be carriedout and the accurate prediction of woods fire insurance grade can be given. Its accurate prediction rate is 89%in spring and 82%in summer. The correctnessand feasibilityare illustratedwith the simulate results.     

Applications of the Fuzzy Neural Networks to Short term Load Forecasting

This paper presents a fuzzy neural network approach to short term load forecasting.It can predict the hourly loads for next day or next week with the fuzzy information.The practical examples have proved the efficiencies of the proposed approach.     

Malfunction Diagnosis of Three-phase Full-bridge Controlled Rectifier Based on BP Neural Network

The diagnosis of the power electronic circuit is very intricate. One of the main reasons is that the structure of the circuit will change if the power device is not working .The thyristor is the easiest to be mangled. So diagnosing the malfunction is the most important about the diagnosis of the power electronic circuit. The paper puts forward a malfunction diagnosis of the thyristors of three-phase full-bridge controlled rectifier with BP neural network. After analyzing the output waveforms of malfunctioning circuit and training a BP neural network with the sampling data of malfunctioning waveforms, a well training BP neural network is constructed and used to diagnose the malfunction. The simulation and experiment demonstrate that this method is valid.     

Fault Diagnosis in Hydraulic Turbine Governor Based on BP Artificial Neural Network

The governing system of hydraulic turbine generator plays an important role in power system. It is significant to find out the faults of governing system and remove them quickly. This paper sets up a new fault diagnosis model of the hydraulic turbine generator governing system with the advanced ANN (artificial neural net). This 17-in-13-out model consists of three layers. It is proved that this model can find the fault accurately.