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As a green renewable resource, wind energy has been emphasized to solve the problem of world energy crisis and environmental pollution. Prediction of the effective wind power density is a significant work for wind power evaluation. In this paper, the effective wind power density values are regarded as a time series, furthermore, Back Propagation Neural Networks (BPNN) and Generalized Regression Neural...
Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function,...
The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive...
Prediction of deformation of foundation pit by means of conventional method such as mechanics analysis or numerical method often has a large error because the deformation process of foundation pit is a highly complicated nonlinear evolution process. A novel method based on Gaussian process (GP) machine learning is proposed for solving the problem of deformation prediction of foundation pit. GP is...
As is known to all, the neutral network has made a great progress in many fields. But due to some strict theoretical system, there are still many defaults in practical application. In this paper, we present an active learning artificial neural network (ALANN). The key issue of this kind of approach is what information can be analysis and forecast about time series(TS). However, the parameters of ALANN...
This study tries to examine the impacts of emotional learning based fuzzy inference system (ELFIS) on completion time of projects. For the project management team, on time delivery within budget is a fundamental and important factor that highlights the importance of estimating the completion time of a project during its execution. This study implies four soft computing methods which are artificial...
Forecasting systems have been widely used for decision making and one of its most promising approaches is based on Artificial Neural Networks (ANN). In this paper, a hybrid swarm system is presented for the time series forecasting problem, which consists of an intelligent hybrid model composed of an ANN combined with Particle Swarm Optimizer (PSO). The proposed method searches the relevant time lags...
This paper introduces a driving danger-level warning system that uses statistical modeling to predict driving risks. The major challenge of the research is how to model the safe/dangerous driving patterns from a sparsely labeled training data set. This paper utilizes both the labeled and the unlabeled data as well as their interdependency to build a proper danger-level function. In addition, the learned...
For nonlinear time series fault prediction online, an incremental learning least squares support machine (LSSVM) is presented to replace LS-SVM which is as a kind of regression method with good generalization ability and trained offline in batch way. The incremented learning LS-SVM fully utilizes historical training results and reduces memory and computation time, which guarantee to predict time series...
Quality and reliability of wireless communication is an actual issue for design of modern high-efficiency information systems in the wide area of human activities. In the paper, the problem of wireless communication reliability and methods of its evaluation are studied. The quality of communication at actual spot is estimated with the method proposed by the authors. It is based on the usage of a prediction...
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