Comparative analysis of neural classifiers for fault detection in DSTATCOM-compensated distribution systems using wavelet features

Digital image recognition of plant diseases could reduce the dependence of agricultural production on the professional and technical personnel in plant protection field and is conducive to the development of plant protection informatization. In order to find out a method to realize image recognition of plant diseases, four kinds of neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used to distinguish wheat stripe rust from wheat leaf rust and to distinguish grape downy mildew from grape powdery mildew based on color features, shape features and texture features extracted from the disease images. The results showed that identification and diagnosis of the plant diseases could be effectively achieved using BP networks, RBF neural networks, GRNNs and PNNs based on image processing. For the two kinds of wheat diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 97.50% with the fitting accuracy equal to 100% while RBF neural networks were used. For the two kinds of grape diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 94.29% with the fitting accuracy equal to 100% while RBF neural networks were used.

This paper uses a radial basis function (RBF) neural network for star identification. A feature vector consisting of distance and angle cosine values is constructed as a library of recognition feature stars and input to the neural network. Then, the network’s output is the number of the corresponding primary star. Comparing the training efficiency and robustness of the grid algorithm with the BP neural network and radial basis function neural network for star identification, the experimental results show that the radial basis function neural network has a strong anti-interference ability against noise and pseudo-star interference. The following conclusions are drawn from the simulation tests: the sample recognition rate of radial basis function neural network algorithm can reach 96% for star position and star addition noise; when the proportion of pseudo-star reaches 20%, the sample recognition rate of radial basis function neural network algorithm is still higher than grid algorithm and BP neural network, which indicates that the radial basis function neural network algorithm has the solid anti-interference ability to both noise and pseudo-star. Therefore, the radial basis function neural network algorithm based on star Identification has specific application prospects.

The Sixth International Symposium on Neural Networks (ISNN 2009)

This article presents the results of a study aimed at the development of a system for short-term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This study indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs when tested on the testing data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 591–605, 2005.

Fault diagnosis for wireless sensor is very important to ensure signal acquisition precision. Radial basis function (RBF) neural network has strong classification ability. However, the selection of the connection weights, the hidden centers and the widths has an important influence on the classification performance of the RBF neural network in the learning process of RBF neural network. Thus, ant colony optimization is employ to gain the parameters of radial basis function neural network. Therefore, a novel method for fault diagnosis of wireless sensor based on RBF neural network and ant colony optimization is presented. The results of computational experiments show that ACO- RBF neural network has a great higher than RBF neural network.

Our work provides an effective feature based method for analyzing both steady state and short duration non-stationary power signal disturbances using a Local Linear Radial Basis Function Neural Network (LLRBFNN). In contrast to the normalized probabilistic neural network (PNN), the proposed LLRBFNN is an excellent approximation network, which performs the classification task with minimal amount of computational power than probabilistic neural network (PNN). The difference of the LLRBFNN with conventional Radial Basis Function Neural Network (RBFNN) is that a local linear model replaces the connection of weights between the hidden layer and output layer of conventional RBFNN. Both normalized time domain and frequency domain features are used for the training purpose. It is noticed that spectral entropy is an effective frequency domain feature for non-stationary power signal classification. Moreover the local linear model provides a robust model for network learning, which is not prone to local linear points. This is supported by the observation that both the LLRBFNN model and the global search optimization techniques like Genetic algorithm provide similar results.

Prediction of biological targets for molecules from their chemical structures is beneficial for generating focused libraries, selecting compounds for screening, and annotating biological activities for those compounds whose activities are unknown. We studied the ability of a probabilistic neural network (PNN), a variant of normalized radial basis function (RBF) neural networks, to predict biological activities for a set of 799 compounds having activities against seven biological targets. The compounds were taken from the MDDR database, and they were carefully selected to comprise distinct biological activities and diverse structures. The structural characteristics of compounds were represented by a set of 24 atom-type descriptors defined by 2D topological chemical structures. The modeling was done in two ways: (1). compounds having one certain activity were discriminated from those not having that activity and (2). all compounds were classified into seven biological classes. In both cases, around 90% of the compounds were correctly classified. Further validation of the modeled PNNs was done with 26 317 compounds having biological activities against various targets except for the seven targets used for modeling, and 67-98% compounds were correctly classified depending upon the targets. A PNN trains much more quickly than widely used neural networks such as a feed-forward neural network with error back-propagation. Calculation of atom-type descriptors is easy even for a large-size chemical library. Combination of PNN and atom-type descriptors thus provides a powerful way to predict biological activities from structural information.

Classical radial basis function (RBF) neural network directly projects input samples into a high dimension feature space through some radial basis functions, and does not take account of the high-order statistical relationship among variables of input samples. But the high-order statistical relationship does play an important part in pattern recognition (classification) area. In order to take advantage of the high-order statistical relationship among variables of input samples in neural network, a novel independent radial basis function (IRBF) neural network is proposed in this paper. Then a new hybrid system combining multiresolution analysis, principal component analysis (PCA) and our proposed IRBF neural network is also proposed for face recognition. According to experiments on FERET face database, our proposed approach could outperform newly proposed ICA algorithm. And it is also confirmed that our proposed approach is more robust to facial expression, illumination and aging than ICA in face recognition.

Power systems are being operated close to their voltage security limit which makes their reliable operation more challenging. Voltage stability has been a major problem faced by utilities now a days. Many blackout have been reported around the world related to voltage instability problem. Many voltage stability analysis methods have been presented up till now. Here artificial neural network based approach for voltage stability analysis of power system is presented. Voltage stability analysis of power system has been done in terms of voltage stability margin. Voltage stability margin is calculated by estimating distance from current operating point to maximum voltage stability point according to loading parameter. Principal Component Analysis is an efficient feature selection method used here to find few input variables required to estimate voltage stability margin with sufficient accuracy and execution speed. Major drawback of previously published work is they need to train new neural network whenever change in system topology occur which is very time consuming process. Therefor in this paper single ANN has been prepared for estimating VSM for several system configuration. Feed forward neural network and radial basis function neural network have been used for voltage stability analysis of power system. The final result indicate that proposed method provide an efficient and compact ANN model for different system configuration as well as base case operating condition with lowest possible input required.

The early diagnosis of induction machine faults has become an exigency in all industrial process, as it reduces the breakdowns occurrence. Therefore, the importance of motor defect detection and their classification has recently result numerous research studies on the application of artificial neural network. In this paper, an effectual radial basis function neural networks (RBFN) and probabilistic neural networks (PNN) based method with dimensionality diminution is suggested for online motor current signal analysis. In order to minimize huge databases, Principal Component analysis is developed for selecting convenient training parameters. The classification performance comparison of RBFN and PNN is applied for the proposed method.

Aiming at the high locating error and the underutilization of redundancy bearing mea surement in Pyroelectric Infrared Sensor (PIR) location system, a novel method of bearing location based on Radial Basis Function Neural Network (RBFNN) is presented. After illustrating the region partition model of PIR sensor node, we take advantage of the K-means clustering method and the gradient-descent method to train the neural network. By comparing different sizes of training samples, we select a neural network model with lower locating error, and we have made a c omparison of RBFNN and the geometric algorithm. The result of simulation shows that the neural network model has 18% higher locating accuracy and the locating error is much less than the geometric algorithm when the target is near the boundary of the detecting area. In troduction With the popularity of PIR's application, researchers come to do researches on target location method based on Wireless Sensor Nerworks (WSNs) and PIR technology. Dr. Qi Hao et al first studied the feasibility of the use of PIR in motion detection. In (1), with the Field of View (FOV) of PIR sensor nodes partitioned into several sub-regions and encoded, the locaction of target is estimated through grid approximation. In (2), a target location estimation method is put forward using the angular bisectors of the FOV. In (3), Probabilistic Neural Network (PNN) is used for assureing which sub-region the target is in. Then the crossing localization based on neural network region partition (CLBNNRP) method is used for estimating the accurate location of the target, which will largely improve the locating accuracy . B y now, most of the target location methods based on PIR focus on geometry and grid approximation algorithms, by measuring the angular information of target through sensor nodes. Geometric Algorithm(GA) is simple and clear, but the bearing error and the distance between target and sensor node may largely lead to loss of locating accuracy with large locating error. Thu s, this paper put forward a bearing location algorithm based on RBFNN. Section 2 presents the sensing model of the detecting area and the codes of every sub-region. Section 3 makes use of the nonlinear mapping relationship between numerous position information of training samples and the angular measurement that PIR sensor nodes acquire to establish a neural network model of target location. Section 4 demonstrates and analyses simulation results. Section 5 concludes the paper and points out the further study.

The study compared the forecasting performance of two multiple-input and multiple-output (MIMO) and two single-input and single-output (SISO) neural networks using 4G network traffic data aggregated into daily, weekly and monthly time spans. To explore the best configuration of SISO and MIMO neural networks, the empirical traffic data of 1-input, 2- input and 3-input were used together with varying the parameters of the models. The study concluded that for 2-input, MIMO Radial basis function neural (RBFN) network performed better than the 2-input MIMO Multilayer perceptron (MLP) neural network in predicting the traffic data. In the case of 3-input, MLP network was found to be more efficient than RBFN network. In the scenario of SISO architecture, the MLP network outperformed the RBFN network for 4G daily, weekly and monthly traffic data.

In the process of moisture content's measurement with microwave resonant cavity based on RBF (radial basis function) neural network, the relationship among resonant frequency, quality factor and environmental temperature (the inputs of network) and moisture content (the output of network) is multiple non-linear. The performance of multiple non-linear regression algorithm is the major factor that determines the accuracy of measurement. A regression algorithm based on a RBF neural network is put forward to improve the measurement result in this paper. The RBF neural network regression algorithm can effectively avoid the BP algorithm disadvantages such as getting into local minimum and the low efficiency, and has the advantages such as high generalization precision and rapid convergence. Consequently, the measurement accuracy is enhanced. In this paper, experimental results show that the high-precision measurement value of moisture content can be obtained with the multiple non-linear regression algorithm based on RBF neural network.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Forecasting for a time series signal carrying single pattern characteristics can be done properly using function mapping-based principle by a well-designed artificial neural network model. But the performances degraded very much when time series carried the mixture of different patterns characteristics. The level of difficulty increases further when there is a need to predict far time samples. Among several possible mixtures of patterns, the trend-cycle time series is having its importance because of its occurrence in many real-life applications like in electric power generation, fuel consumption and automobile sales. Over the mixed characteristics of patterns, a neural model, suffered heavily in getting generalized learning, in result poor performances appeared over test data. To overcome this issue in this work, a decomposition-based approach has been applied to separate the component patterns of trend and cyclic patterns, and a dedicated model has been developed for predicting the individual data patterns. The linear characteristic of the trend data pattern has been modeled through a linear regression model while the nonlinearity behavior of cyclic pattern has been model by an adaptive radial basis function neural network. The final predicted outcome has been considered as the linear combination of individual model outcomes. The Gaussian function has been considered as the kernel function in the radial basis function neural network because of its wider and efficient applicability in function mapping. The performance of the neural model has been improved very much by providing the adaptive value of spreads and centers of basis function along with weights values. In this paper, two different applications of forecasting in the area of electric power demand by the individual house and month-wise annual power generation have been considered. Based on house characteristics parameters, the power demanded by a house have been considered which carried a moderate complexity of function mapping problem while in another case, total power generation needed to be predicted on the monthly basis for a year from just the previous year observation, which carried the mixed behavior of trend and cyclic pattern. For house power demand forecasting the adaptive kernel-based radial basis function has shown very satisfactory performances and much better against static kernel radial basis function and multilayer perceptron neural network. The integrated approach of neural model and linear regression has shown very efficient outcomes for the mixture pattern while individual neural models were failed to do so. The coefficient of determination (R2) has been applied to estimate the quality of predicted outcomes and comparison.