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A huge amount of data concerning the position of individual is often gathered in surveillance scenarios, to prevent crimes or to collect evidence of unlawful behaviour. Given the aboundance of data available, detectives need advanced analysis means in order to set apart the interesting locations. This paper proposes a solution that makes use of radial basis neural networks to find the points of interests,...
A significant increase in the accuracy of hyper spectral image classification has been achieved by using ensembles of radial basis function networks trained with different number of neurons and different distance metrics. Best results have been obtained with Gamma-divergence distance metrics. In this paper, previous work is extended by evaluation of different approaches for the fusion of the multiple...
In this paper we investigate the performance of a hybrid learning algorithm for RBF network in the application of short-term load forecasting. In this method the algorithm for finding radial basis function centers of hidden layer is k-means and the algorithm for training the weights of output layer is adaptive variable step-size algorithm. We proved this method is both accurate and fast in comparison...
Facial expression analysis and recognition have been researched since the 17'th century. The foundational studies on facial expressions, which have formed the basis of today's research, can be traced back to few centuries ago. Precisely, a detailed note on the various expressions and movements of head muscles was given in 1649 by John Bulwer(1). Another important milestone in the study of facial expressions...
This paper proposes a radial basis function (RBF) network trained using ridge extreme learning machine to predict the future trend from the past stock index values. Here the task of predicting future stock trend i.e. the up and down movements of stock price index values is cast as a classification problem. Recently extreme learning machine (ELM) is used as an efficient learning algorithm for single...
In order to improve the accuracy of INS/GPS integrated navigation system during GPS signals blockage, an effective and low-cost method is to design the corresponding linear or non-linear predictor to predict the position and velocity errors between INS and GPS during GPS blockage and then to correct the results of INS. Based on the distributed data fusion system, a novel hybrid prediction method that...
The task of predicting the stature of human skeletal remains using bone measurements is an important one in bioarchaeology. Classical attempts to solve this problem mostly consist of linear regression formulas on various bone lengths. In order to improve these results, we propose using locally-weighted regression and radial basis function networks in order to fit the available data better, especially...
In a fuzzy neural network, a fuzzy rule may be active in early stage, then the contribution of the rule to system become small. In this paper, A Self Adaptive incremental learning Fuzzy Neural Network Based on the Influence of a Fuzzy Rule (SAIL-FNN) is developed. In SAFIS, the concept of "influence" of a fuzzy rule is introduced and fuzzy rules are added or removed based on the influence...
Because of the volatility and intermittent of photovoltaic (PV) power, in order to meet the requirement of grid planning, the prediction of PV systems not only need to provide the exact outcome of the predicted value, but also need to make a reasonable assessment for the risk including predicted value. This paper proposes a nonparametric method for construction of reliable Prediction Intervals (PIs)...
Sentiment analysis of tweets requires the ability to reliably and accurately identify the emotional polarity (positive or negative) of instances. This can be challenging, particularly when the data quality is questionable due to noise or imbalance. Ensemble learning algorithms have been shown to offer superior performance compared to non-ensemble techniques in many domains, but have not been thoroughly...
The calculation of the closure of human eyes is commonly adopted to detect driver fatigue. In order to realize human eyes closure calculation, correct and rapid detection of human face is accomplished firstly, for the specific environment of cabs, this paper proposes a fast face detection algorithm based on skin color model and radial basis function network, which makes input image carry out RGB and...
The paper addresses the problem of the radial basis function network initialization with feature section carried-out independently for each hidden unit. In each case a unique subset of features is derived from respective clusters of instances using the rotation-based ensembles technique. The process of the RBFN design with cluster-dependent features, including initialization and training, is carried-out...
This paper proposes a new algorithm for blocking the operation of Indirect Symmetrical Phase Shift Transformer (ISPST) differential relay when subjected to different operating conditions except internal fault condition. The proposed algorithm is amalgamation of Phase angle shift (PAS) threshold and Optimal Radial Basis Function Neural Network (ORBFNN). PAS between source and load side currents of...
Conventional learning theory's failure in training Neural Network to provide acceptable levels of generalization on the occurrences of fault in network has lead to the advent of Fault Tolerant Learning. Radial Basis Function networks are assumed to have in built Fault Tolerance capabilities. With this paper our attempt is to bring forth a detailed and time ordered survey of the literature available...
This paper presents a new approach for 3-D object segmentation. Objects from a stack of images are represented as overlapping ellipsoids. Graylevel statistics and shape features are simultaneously employed for object modeling in an unsupervised approach. The extension of the Hough Transform in the 3-D space is used for finding the ellipsoid centers. Each ellipsoid is modeled by a Radial Basis Function...
This paper presents a neural identification method of traffic participants at airports. The traffic objects are observed by several dissimilar sensors. Their information is fused to possible object feature sets. These feature sets are processed by a neural network based identifier, so that traffic participants are identified on the basis of very few observations. The neural network identifier is composed...
In this paper, the use of clustering algorithms for decision level data fusion is proposed. Person authentication results coming from several modalities (e.g. still image, speech), are combined by using the fuzzy k-means (FKM) and the fuzzy vector quantization (FVQ) algorithms, two modification of them that use fuzzy data FKMfd and FVQfd, and a median radial basis function (MRBF) network. The modifications...
A new fast estimation algorithm is derived for identification of non-linear dynamic systems using radial basis function networks. The new algorithm is able to both select the hidden nodes and to compute weights of the output layer in the radial basis function neural networks (RBFN) simultaneously in a stepwise forward fashion.
Fraud detection in telecommunications is an area where pattern recognition and so called “intelligent” techniques have found widespread use. Due to fraud, companies suffer not only direct economic losses but also the risk of bad publicity. In this paper real cases of fraud are being treated in order to develop a detection system with low number of false alarms and good sensitivity. Call data records...
This paper addresses the classification of multispectral remote-sensing images by the neural-network approach. In particular, an experimental comparison on the performances provided by different neural models for classifying multisensor remote-sensing data is reported. Four neural classifiers are considered in the comparison: the Multilayer Perceptron, Probabilistic Neural Networks, Radial Basis Function...
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