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Data mining is a technology in data analysis with rising application in sports. Basketball is one of most popular sports. Due to its dynamics, a large number of events happen during a game. Basketball statisticians have task to note as many of these events as possible, in order to provide their analysis. In this paper, we used data from the First B basketball league for men in Serbia, for seasons...
It is very important to forecast the ice thickness of Transmission Line for the safe operation of transmission network. The author had introduced artificial neural network(ANN) to the prediction of the ice thickness of transmission line, and proposed a predictive model based on GA and BP addresses on the defects of BP network includes slow convergence and easiness of running to local minimum, and...
In this paper, through combining information diffusion principle and BP neural network theory, a new prediction model of drought disaster assessment is established. First, the original data are fuzzily processed based on information diffusion method, then a new training sample is formed; second, the new sample is used to design and train BP neural network; finally, the trained fuzzy neural network...
Gas filow-volume controlled by many factors, the trend is complex, the accurate mathematical model to predict, in view of this situation, the paper attempts to grey dynamic model based on artificial neural network, organic combination of intelligent analysis method, structural gray neural network combination forecast model, based on Visual Basic 6.0, meanwhile, corresponding calculation program is...
For the current problems in the security situation of colliery equipment ,and based on non-linear relationship among the parameters of colliery equipment ,this paper presents a method for forecasting the safety of colliery equipment based on BP neural network.By using BP neural network in the colliery safety equipment monitoring and warning issues,we established a multi-index comprehensive monitoring...
The drilling of hard-to-cut high manganese steel materials is a difficulty in the field of machining. Research method for drilling temperature which has been commonly used is experimental method. The method has long-time and the high-cost drawbacks. Adopting error back neural network technology and using Matlab and C language programming method, in this paper neural network prediction model of drilling...
SVM is powerful for the problem with small samples, non linear and high dimension. But such important parameters as the kernel function parameters, the insensitive parameters and the penalty coefficient are determined based on experience and cross-validation in the SVM, so it has certain blindness. In the paper, support vector machine optimized particle algorithm is used to predict the intensity of...
Short-term prediction of intelligent traffic flow is in favor of road unblocked and vehicle waiting strategy. The algorithm of short-term prediction of intelligent traffic flow based on back propagation(BP) neural network and autoregressive integrated moving average (ARIMA) model can solve it partially. Firstly, Establishing a BP neural network sub-model and ARIMA sub-model, Then taking BP neural...
Multi-label classification is a popular learning task. However, some of the algorithms that learn from multi-label data, can only output a score for each label, so they cannot be readily used in applications that require bipartitions. In addition, several of the recent state-of-the-art multi-label classification algorithms, actually output a score vector primarily and employ one (sometimes simple)...
Various attribute and relation information is used in social recommendation systems. However, previous approaches fail to use them in a unified way. In this paper, we propose a unified framework for social recommendation. Entities like users and items are described by their tags. We model each entity using topic models like Latent Dirichlet Allocation(LDA) and then connect these topic models to form...
Ensemble pruning is concerned with the reduction of the size of an ensemble prior to its combination. Its purpose is to reduce the space and time complexity of the ensemble and/or to increase the ensemble's accuracy. This paper focuses on instance-based approaches to ensemble pruning, where a different subset of the ensemble may be used for each different unclassified instance. We propose modeling...
With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its...
An application of Parallel Radial Basis Function (PRBF) network model on prediction of chaotic time series is presented in this paper. The PRBF net consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of PRBF is a weighted...
The paper achieved the application of rough set-SVM model in the prediction of supply chain performance evaluation. Firstly, the paper rejected redundant factors and extracted key factors by use of rough set theory; then, safe class of supply chain performance evaluation was gained based on the key factors which have achieved with the method of SVM (support vector machines). In the end, result of...
Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) post-dialysis blood urea or...
In this paper classification of chip form and main cutting force prediction of cast nylon in turning operation by using artificial neural network (ANN) are described. The multi-layer perceptron of back-propagation neural network (BPNN) was employed as a tool to classify a chip form following ISO 3685-1977(E) and predicted the tangential cutting force. The turning operation was performed by a conventional...
The present paper employs a particle swarm optimization (PSO) based adaptive linear combiner for efficient prediction of various stock indices in presence of strong outliers in the training data. The connecting weights of the model are updated by minimizing the Wilcoxon norm of the error vector by PSO. The short and long term prediction performance of the new model is evaluated with test data and...
The paper aims to develop an efficient forecasting model using differential evolution (DE) based learning rule. The structure chosen is an adaptive linear combiner whose weights are trained using DE. The prediction performance of the resulting model is evaluated by feeding features of retail sales data for different months' ahead prediction. These results are compared with those obtained by GA based...
This paper presents a novel application of the self-organised multilayer perceptrons inspired by the immune algorithm in financial time series prediction. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK exchange rates, the JP/US exchange...
Given a random binary sequence X(n) of random variables, Xt, t = 1, 2, ..., n, for instance, one that is generated by a Markov source (teacher) of order k* (each state represented by k* bits). Assume that the probability of the event Xt = 1 is constant and denote it by ??. Consider a learner which is based on a parametric model, for instance a Markov model of order k, who trains on a sequence x(m)...
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