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The paper presents the two-stages adaptive approach for short-term forecast of parameters of expected operating conditions. The first stage involves decomposition of the time series into intrinsic modal functions and subsequent application of the Hilbert transform. During the second stage the computed modal functions and amplitudes are employed as input functions for artificial neural networks. Their...
A high accurate wind speed forecasting can effectively reduce or avoid the adverse effect of wind farm on power grid, meanwhile enhances the competitive ability of wind power in electricity market. In this paper, a short-term wind speed forecasting method based on auto-regressive integrated moving average (ARIMA) and least square support vector machine (LS-SVM) is proposed. The weights are calculated...
The framework of adaptive control applied to a wind turbine is presented. The wind turbine is adaptively controlled to achieve a balance between two objectives, power maximization and minimization of the generator torque ramp rate. An optimization model is developed and solved with a linear weighted objective. The objective weights are autonomously adjusted based on the demand data and the predicted...
The procedure and main result of a comparative study based on using an autoregressive model and an artificial intelligence technique applied to a Wimax traffic data series forecasting task are presented in this document. The time series forecasting methods being compared are: ANFIS model (Adaptive Network-based Fuzzy Inference Sys-tem) and ARIMA model (Auto-Regressive Integrated Moving Average). This...
This paper presents the application of the Lowered EXCELL model to discriminate between stratiform and convective precipitation in Kuala Lumpur, Malaysia, which is located in the equatorial region. The model generates two longterm cumulative distribution functions (CDFs) that separately account for the two different types of rain, based on the input rainfall statistics reflecting the local climatology...
Considering of the ill-posed problem in learning process of echo state network(ESN), a new learning algorithm of ESN is proposed based on regularization method. The regularization term provides a stable solution to function approximation with a tradeoff between accuracy and smoothness of the solutions. So the redundant weights of neural network are damped and converged to the zero state. The structure...
Prediction models based on different concepts have been proposed in recent years. The accuracy rates resulting from linear models such as exponential smoothing, linear regression (LR) and autoregressive integrated moving average (ARIMA) are not high as they are poor in handling the nonlinear relationships among the data. Neural network models are considered to be better in handling such nonlinear...
The fuzzy time series is introduced by Song and Chissom to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect...
This paper represents a fusion model of functional link artificial neural network (FLANN) based on Kernel Regression (KR) for modeling and prediction of exchange rate time series. To predict the exchange rate, we process the exchange rate datasets with KR to smooth the noise. And then the smoothed datasets are nonlinearly expanded using the sine and cosine expansions before inputting to the FLANN...
An approach for building T-S fuzzy model is proposed based on fuzzy c-mean clustering algorithm on the basis of nonlinear modeling experience. An alternative T-S fuzzy model is adapted, which has the uniformed premise structure, the premise parameter is decided by fuzzy c-mean clustering algorithm and the consequence parameters is calculated by least square algorithm, and the identification precision...
This article describes a novel framework for combining time series forecasts. It uses neural network regression models to estimate, at a given point in time, the linear weights (relevancies) of the available experts (forecasters) at that time. With those weights, the experts can be linearly combined to produce a single, potentially more accurate, forecast. This new weight generation framework was...
The wavelet multi-resolution analysis is combined with time series traffic flow models. The characteristic of traffic flow is decomposed by wavelet multi-resolution analysis. According to the long relativity of smooth component and the short relativity of detail component, corresponding models are set up. The prediction of traffic flow series is implemented by synthesizing all the components. Experiments...
Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper. Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter β based on the trend component of empirical...
This paper selected the raw data of the corn output of Dehui City, Jilin Province from 1990 to 2000, through data cleansing, data conversion and data integration technologies obtains time series data set, choosing the appropriate time series methods ARIMA(Autoregressive Integrated Moving Average) to confirm the corn output in a time series prediction model. The experimental results show that comparing...
Analyzing and predicting with Time series is a method which used in different fields, including consumption pattern analyzing and predicting. In this paper, required amount of inventory items have been predicted with time series. At first, desired data mining process is designed and implemented using Clementine data mining tool. We evaluate this process using the dataset from Iran's ZoabAhan steel...
In this paper we have applied the adaptive neuro-fuzzy inference system (ANFIS) which is realized by an appropriate combination of fuzzy systems and neural networks for forecasting a set of input and output data of Internet traffic time series. Several statistical criteria are applied to provide the effectiveness of this model. The obtained results demonstrate that the ANFIS model present a good precision...
Trusted and reliable wireless sensor networks (WSNs) rely on the accurate and rapid detection of anomalies. However, the security model in wired networks is not suited for WSNs because of their energy constraints. In this paper, a traffic prediction model based on gene expression programming (GEP-ADS) is proposed, to predict the time series of normal traffic. Then we present a lightweight anomaly...
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...
The majorities of the existing predictors for states are model-dependent and therefore require some prior knowledge for the identification of complex systems, usually involving system identification, extensive training or online adaptation in the case of time-varying systems. In this paper a model-free predictor (MFP) for a time series produced by an unknown nonlinear system or process is proposed...
In this paper we apply the nonlinear time series prediction method to the traffic measurements data. Based on the phase space reconstruction, the support vector machine prediction method is used to predict the traffic measurements data, and the neighbor point selection method is used to choose the number of nearest neighbor points for the support vector machine regression model. The experiment results...
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