PSO-RBFNN based optimized PNN classifier model

An offline optimization approach based on energy storage management response in a microgrid was not fast and not reliable enough to control and adjust the system efficiently after the loss of the utility grid. Thus, it causes system inefficiency and collapse in the presence of violent changes of loads or outage of distributed generations. To solve such a problem, more real-time management is needed. Any changes in loads/generations should be compensated successfully by a battery energy storage system (BESS) in a short period of time. This paper presents a new method for the intelligent online management of both active and reactive power of a BESS based on a radial basis function neural network (RBFNN) incorporating particle swarm optimization (PSO) to prevent the stand-alone microgrid from instability and system collapse. BESS is centrally controlled by a controller developed by the proposed RBFNN. PSO is used to determine the optimized active and reactive power at every load/generation changing situation to monitor the effect of system frequency, voltage, and reference power regulation. These optimized power data are then employed as target data for the RBFNN generalization and training process. To enable the online updating of the operating parameters, the proposed RBFNN is implemented in the management process. With an appropriate RBFNN training, the optimum active and reactive power can directly be obtained without the necessity of performing the PSO optimization process at any change of load/generation. The results show that the predictive results of the proposed RBFNN model only slightly differed from the target results based on PSO and have a minimum statistical error compared to the predictive results based on the multilayer perceptron neural network (MLPNN) model. The proposed RBFNN is suitable for the online estimation of the active and reactive power of BESS and can be used for real-time energy storage management as an online controller.

The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction.

The identification of nonlinear systems with artificial neural networks models has been successfully used in many applications. Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models for nonlinear systems is vital in many fields of engineering. The Radial Basis Function Neural Network (RBF-NN) is a powerful approach for nonlinear identification and can be improved using Particle Swarm Optimization (PSO) approaches. This paper presents a multivariable nonlinear system identification using RBF-NN combined with standard PSO and Constriction Factor PSO (CFPSO) approaches in order to determine the RBF-NN parameters. RBF-NN is considered to be a good choice for black-box modeling problems due to its rapid learning capacity and, therefore, has been applied successfully to nonlinear time series modeling and nonlinear identification. On the other hand, PSO was inspired by the choreography of bird flocks and fish schools and can be seen as a distributed behavior algorithm that performs multidimensional search. Furthermore, promising simulation results from performance analysis of the proposed RBF-NN with PSO training approaches are presented and discussed in this paper showing promising results.

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.

Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis function neural network with particle swarm optimization algorithm is developed. The combination method of RBF neural network and particle swarm optimization is applied to fault diagnosis of networked synchronization control system. The test results indicate that the combination model of RBF neural network and particle swarm optimization can almost entirely recognize each state of the device in networked synchronization control system. The diagnostic accuracy of the combination model of RBF neural network and particle swarm optimization is greater than that of normal RBF neural network.

This paper investigates the effect of segmentation errors on off-angle iris recognition. We first segment the inner and outer boundaries of off-angle iris images by fitting the best ellipses to boundaries. Then, we validate the segmentation results using Ground Truth Tool and fix the possible errors. Second, we add random errors on iris segmentation parameters to examine how Hamming distance distribution changes for different amount of segmentation error. In order to add errors, we first group the parameters into three sets as ellipse center (i.e., x, y), minor and major axis (i.e., r 1 , r 2 ) and orientation (θ) and changed the parameters by adding positive and negative random error as a noise to the ground truth segmentation parameters. Our purpose is to show how performance of iris recognition is affected if there is an error in segmentation and how important is to have a very robust segmentation algorithm for off-angle iris recognition. We test the effect of segmentation errors using our off-angle iris dataset that contains images from -50° to +50° in gaze angles. Based on our experimental results, we observed that error in ellipse center decrease the recognition performance worse than minor and major axis and orientation. The main reason is the normalization process tolerates the small amount of error in minor and major axis and bit shift in matching handles the small variations in the orientation errors. However, if the error in the ellipse orientation exceeds the limit of the bit shift method tolerance, the performance of the off-angle iris recognition dramatically drops.

There are many methods can be used to calculate ship's magnetic field such as harmonic wave analysis, finite element method, integral equation method, boundary element method and so on. But these methods generally have disadvantage of complex calculation and poor universality. In this paper, we have built a predicted model based on improved Radial Basis Function (RBF) neural network to study the relationship between ship's important parameters and magnetic field directly. Generally, the parameters of RBF are set by experience. In order to get the best result, we have chosen the spread parameter according to Particle Swarm Optimization (PSO) algorithm and verified the effect of network by an example.

Modeling research on wheat protein content measurement using near-infrared reflectance spectroscopy and optimized radial basis function neural network

Scene classification is one of the most significant and challenging tasks in computer vision. This paper presents a new method for scene classification using bag of visual words and a particle swarm optimization (PSO)-based artificial neural network classifier. Contributions of this paper are introducing a novel feature integration method using scale invariant feature transform (SIFT) and local binary pattern (LBP) and a new framework for training radial basis function neural network, combining optimum steepest decent method with a specially designed PSO-based optimizer for center adjustment of radial basis function neural network. Our study shows that using LBP increases the performance of classification task compared to using SIFT only. In addition, our experiments on Proben1 dataset demonstrate improvements in classification performance (averagely about 6.04%) and convergence speed of the proposed radial basis function neural network. The proposed radial basis function neural network is then employed in scene classification task. Results are reported for classification of the Oliva and Torralba, Fei–Fei and Perona and Lazebnik et al. datasets. We compare the performance of the proposed classifier with a multi-way SVM classifier. Experimental results show the superiority of the proposed classifier over the state-of-the-art on the three datasets.

To accomplish the effective classifier and secure the accurate classification capabilities of black plastics, a comprehensive design methodology of fuzzy radial basis function neural networks is developed with the aid of principal component analysis and particle swarm optimization. Plastics recycling is the competitive method which can deal with the shortage of natural resource. To recycle and reuse the waste plastics, this study is given as the key issue to identify and classify waste plastics by resin type such as polyethylene terephthalate, polypropylene, polystyrene, etc. To complement the weak points of recognition and classification of the near-infrared radiation equipment, Raman spectroscopy is used to obtain qualitative as well as quantitative analysis of black plastics. To improve the identification performance of black plastics, an intelligent computing algorithm such as fuzzy radial basis function neural networks classifier and preprocessing algorithm as principal component analysis are applied to analyze and classify the obtained spectrum of black plastics. Finally, to optimize the structure as well as parameters of fuzzy radial basis function neural networks, particle swarm optimization technique is used. The obtained experimental results show that the proposed network architecture exhibits high classification capabilities in practical applications.

ECG beat classification using particle swarm optimization and radial basis function neural network

Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology.

An improved radial basis function neural network for object image retrieval

Early detection and prediction of fatal heart diseases can be useful for medical diagnosis and rehabilitation. In this study, a hybridized Particle Swarm Optimization (PSO) and Radial Basis Function Neural network (RBFN) model has been proposed to improve cardiac arrhythmia prediction accuracy. The performance of RBFN is very sensitive to parameter such as spread. In RBFN training process, the spread is optimized using PSO. In the proposed method a set of linear and non-linear features are extracted from the RR interval time series datasets, which are derived from the MIT-BIH arrhythmia database. Experiments are carried out on the heart rate (RR Interval) time series to predict the eight types of cardiac signals that contain normal and abnormal rhythms. The obtained results suggest that PSO-RBFN model may be an efficient tool to achieve enhancements in terms of prediction accuracy for diagnosis of cardiac disorders. The hybrid PSO-RBFN model yielded an overall prediction accuracy of 96.3%.