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This paper focuses on distributed implementation of active learning with a limited number of queries. In the prognostics and health management domain, the cost to obtain a training sample can be fairly high, especially when studying the aging process for remaining useful life prediction of a mission critical component. Active learning with limited resource is formulated as a reinforcement learning...
A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified factors of gas emission analyzed have grey correlation. As a result, the model designed using this method...
Although many models have been developed for prediction and forecasting of time series in various engineering fields, there is no perfect model to forecast hydrologic time series. In recent decades, Artificial Neural Networks (ANNs) have been very common for prediction and forecasting of hydrologic time series because of their practicality in applications. In this paper, we propose the application...
Physical activity (PA) can influence heart rate(HR). But the relationship between HR and PA is hard to describe. In our previous works, HR prediction models based on PA were designed. However, the prediction time length and accuracy are usually hard to compromise. In this study, a new HR prediction method is proposed. The predicted HR is used as the input in the next prediction step. Only HR at the...
Related data streams refer to data streams that can be joined together by matching their join attributes. Existing research on learning from related data streams is based on an assumption that all streams arrive at a central processing unit in a synchronous way, such that in an arbitrary sliding window, all tuples of the streams can be perfectly joined together. This assumption, however, does not...
This study proposed a novel HPSO-SVR model that hybridized the particle swarm optimization (PSO) and support vector regression (SVR) to improve the regression accuracy based on the type of kernel function and kernel parameter value optimization with a small and appropriate feature subset, which is then applied to forecast the monthly rainfall. This optimization mechanism combined the discrete PSO...
The traditional prediction models of business failure are usually constructed upon the research sample without missing values, that is, the training and testing procedure of the prediction model are not able to be completed if some observations of the relevant variables are missing. This study solves this problem by applying for the data imputation technique of which the autoassociative neural networks...
Because of globalization, fast changes of technology and short life cycle of products, enhancing the accuracy of demand forecasts becomes one of the important issues for managers. The objective of this paper is to analyze and explore given data of orders using adaptive neuro-fuzzy inference system (ANFIS) and to draw up, by ANFIS learning mechanism, the relational rules from historical order data,...
Nowadays there are lots of novel forecasting approaches to improve the forecasting accuracy in the financial markets. Support Vector Machine (SVM) as a modern statistical tool has been successfully used to solve nonlinear regression and time series problem. Unlike most conventional neural network models which are based on the empirical risk minimization principle, SVM applies the structural risk minimization...
The ultimate goal in a multiple classifier system (MCS) is to obtain a global and more accurate model through the combination of several base learners. Among the popular combining rules, averaging has been emphasized as a well qualified option. The averaging rule can be applied with equal (simple averaging) or non-equal (weighted averaging) weights vector for the linear combination. When the formed...
Throughout the 1990s, four global waves of financial turmoil occurred. The beginning of the 21st century has also suffered from several crisis episodes, including the severe sub prime crisis. However, to date, the forecasting results are still disappointing. This paper examines whether new insights can be gained from the application of the Self-Organizing Map (SOM) - a non-parametric neural network-based...
Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
The monitoring and management of the high density crowd in large scale public place is an important factor of city disaster reduction and mitigation. Automatic short term prediction of crowd density is a key problem. This paper introduces a prediction algorithm using v-support vector regression (v-SVR), which can control the accuracy of fitness and prediction error by adjusting the parameter v. An...
Spectrum sensing is one of the core techniques in the cognitive radio network. In this paper, the cyclostationary feature detection for OFDM signals and the MAC-layer sensing-period adaptation algorithm are presented. Exact analytical expressions for spectral correlation function (SCF) are derived applying cyclostationarity fundamentals, and the simulation results demonstrate that the presence of...
Accurate and precise prediction of traffic variables such as speed, volume, density, travel time, headways etc. is important in traffic planning, design, operations, etc. Short term prediction of these variables plays a very important role in Intelligent Transportation Systems (ITS) applications. Under Indian scenario, this short term prediction of traffic variables has gained greater attention with...
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...
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 recognition of prosodic structure is an important research aspect in the field of Text-to-Speech. It is essential to improving the naturalness of machine-synthesized speech. This paper proposes an approach to predicting and assigning prosodic structure automatically for Chinese sentences based on their tree structures. It presents the modeling of a statistical language model based on the simply...
The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured...
While drilling with PDC Bit, Some problems are come out when using the methods of traditional recorder well because Lithology is hard to identify. The data collected by mud-logging equipment, for examples, ROP, WOB, torque et al., reflects the different properties of rock in the formation of subsurface. The relation among them is complex and BP neural network is adopted in three layers. The depth...
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