239,643 research outputs found
A prediction approach for multichannel EEG signals modeling using local wavelet SVM
Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.published_or_final_versio
Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM)
Forecasting Models for Integration of Large-Scale Renewable Energy Generation to Electric Power Systems
Amid growing concerns about climate change and non-renewable energy sources deple¬tion, vari¬able renewable energy sources (VRESs) are considered as a feasible substitute for conventional environment-polluting fossil fuel-based power plants.
Furthermore, the transition towards clean power systems requires additional transmission capacity. Dynamic thermal line rating (DTLR) is being considered as a potential solution to enhance the current transmission line capacity and omit/postpone transmission system expansion planning, while DTLR is highly dependent on weather variations. With increasing the accommodation of VRESs and application of DTLR, fluctuations and variations thereof impose severe and unprecedented challenges on power systems operation. Therefore, short-term forecasting of large-scale VERSs and DTLR play a crucial role in the electric power system op¬eration problems. To this end, this thesis devotes on developing forecasting models for two large-scale VRESs types (i.e., wind and tidal) and DTLR.
Deterministic prediction can be employed for a variety of power system operation problems solved by deterministic optimization. Also, the outcomes of deterministic prediction can be employed for conditional probabilistic prediction, which can be used for modeling uncertainty, used in power system operation problems with robust optimization, chance-constrained optimization, etc. By virtue of the importance of deterministic prediction, deterministic prediction models are developed. Prevalently, time-frequency decomposition approaches are adapted to decompose the wind power time series (TS) into several less non-stationary and non-linear components, which can be predicted more precisely. However, in addition to non-stationarity and nonlinearity, wind power TS demonstrates chaotic characteristics, which reduces the predictability of the wind power TS. In this regard, a wind power generation prediction model based on considering the chaosity of the wind power generation TS is addressed. The model consists of a novel TS decomposition approach, named multi-scale singular spectrum analysis (MSSSA), and least squares support vector machines (LSSVMs).
Furthermore, deterministic tidal TS prediction model is developed. In the proposed prediction model, a variant of empirical mode decomposition (EMD), which alleviates the issues associated with EMD. To further improve the prediction accuracy, the impact of different components of wind power TS with different frequencies (scales) in the spatiotemporal modeling of the wind farm is assessed. Consequently, a multiscale spatiotemporal wind power prediction is developed, using information theory-based feature selection, wavelet decomposition, and LSSVM.
Power system operation problems with robust optimization and interval optimization require prediction intervals (PIs) to model the uncertainty of renewables. The advanced PI models are mainly based on non-differentiable and non-convex cost functions, which make the use of heuristic optimization for tuning a large number of unknown parameters of the prediction models inevitable. However, heuristic optimization suffers from several issues (e.g., being trapped in local optima, irreproducibility, etc.). To this end, a new wind power PI (WPPI) model, based on a bi-level optimization structure, is put forward. In the proposed WPPI, the main unknown parameters of the prediction model are globally tuned based on optimizing a convex and differentiable cost function. In line with solving the non-differentiability and non-convexity of PI formulation, an asymmetrically adaptive quantile regression (AAQR) which benefits from a linear formulation is proposed for tidal uncertainty modeling. In the prevalent QR-based PI models, for a specified reliability level, the probabilities of the quantiles are selected symmetrically with respect the median probability. However, it is found that asymmetrical and adaptive selection of quantiles with respect to median can provide more efficient PIs. To make the formulation of AAQR linear, extreme learning machine (ELM) is adapted as the prediction engine. Prevalently, the parameters of activation functions in ELM are selected randomly; while different sets of random values might result in dissimilar prediction accuracy. To this end, a heuristic optimization is devised to tune the parameters of the activation functions.
Also, to enhance the accuracy of probabilistic DTLR, consideration of latent variables in DTLR prediction is assessed. It is observed that convective cooling rate can provide informative features for DTLR prediction. Also, to address the high dimensional feature space in DTLR, a DTR prediction based on deep learning and consideration of latent variables is put forward.
Numerical results of this thesis are provided based on realistic data. The simulations confirm the superiority of the proposed models in comparison to traditional benchmark models, as well as the state-of-the-art models
BACKPROPAGATION NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI HARGA KARET SPESIFIK TEKNIS
Rubber is the commodity of the results of demand levels andagricultural production always has increased significantly from time to time.This is due to the high demand of the company's suppliers are the result ofprocessed rubber to meet the needs of production. However, due to theinfluence of the conditions of the global economy so that it appears theinstability of prices. The data used in this research in the form of aUnivariate time series data is converted into the multivariate. The methodused is the method of Back propagation Neural Network (BPNN) is appliedto the data time series technical specific rubber commodity prices with thehelp of weighted optimization Particle Swarm Optimization (PSO) whichhopefully may help to improve the performance of the prediction so thatresults of the RMSE for the prediction of rubber prices gained can be moreaccurate. Of research results obtained the best model on a back propagationneural network with the parameters for the training cycle 600, the learningrate and momentum 0.1 0.2, as well as neuron size 3 whereas in particleswarm optimization value of population size 8, max value. of generation 100,the value of inertia weight 0.3, the value of the local best weight 1.0 andglobal best value weight of 1.0 produces a better RMSE value i.e. 0.040compared to just using the BPNN alone i.e. 0043. This proves that the PSOmethod able to give better results
Basin structure of optimization based state and parameter estimation
Most data based state and parameter estimation methods require suitable
initial values or guesses to achieve convergence to the desired solution, which
typically is a global minimum of some cost function. Unfortunately, however,
other stable solutions (e.g., local minima) may exist and provide suboptimal or
even wrong estimates. Here we demonstrate for a 9-dimensional Lorenz-96 model
how to characterize the basin size of the global minimum when applying some
particular optimization based estimation algorithm. We compare three different
strategies for generating suitable initial guesses and we investigate the
dependence of the solution on the given trajectory segment (underlying the
measured time series). To address the question of how many state variables have
to be measured for optimal performance, different types of multivariate time
series are considered consisting of 1, 2, or 3 variables. Based on these time
series the local observability of state variables and parameters of the
Lorenz-96 model is investigated and confirmed using delay coordinates. This
result is in good agreement with the observation that correct state and
parameter estimation results are obtained if the optimization algorithm is
initialized with initial guesses close to the true solution. In contrast,
initialization with other exact solutions of the model equations (different
from the true solution used to generate the time series) typically fails, i.e.
the optimization procedure ends up in local minima different from the true
solution. Initialization using random values in a box around the attractor
exhibits success rates depending on the number of observables and the available
time series (trajectory segment).Comment: 15 pages, 2 figure
Local Short Term Electricity Load Forecasting: Automatic Approaches
Short-Term Load Forecasting (STLF) is a fundamental component in the
efficient management of power systems, which has been studied intensively over
the past 50 years. The emerging development of smart grid technologies is
posing new challenges as well as opportunities to STLF. Load data, collected at
higher geographical granularity and frequency through thousands of smart
meters, allows us to build a more accurate local load forecasting model, which
is essential for local optimization of power load through demand side
management. With this paper, we show how several existing approaches for STLF
are not applicable on local load forecasting, either because of long training
time, unstable optimization process, or sensitivity to hyper-parameters.
Accordingly, we select five models suitable for local STFL, which can be
trained on different time-series with limited intervention from the user. The
experiment, which consists of 40 time-series collected at different locations
and aggregation levels, revealed that yearly pattern and temperature
information are only useful for high aggregation level STLF. On local STLF
task, the modified version of double seasonal Holt-Winter proposed in this
paper performs relatively well with only 3 months of training data, compared to
more complex methods
A data-driven based decomposition?integration method for remanufacturing cost prediction of end-of-life products
Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products
Local-Aggregate Modeling for Big-Data via Distributed Optimization: Applications to Neuroimaging
Technological advances have led to a proliferation of structured big data
that have matrix-valued covariates. We are specifically motivated to build
predictive models for multi-subject neuroimaging data based on each subject's
brain imaging scans. This is an ultra-high-dimensional problem that consists of
a matrix of covariates (brain locations by time points) for each subject; few
methods currently exist to fit supervised models directly to this tensor data.
We propose a novel modeling and algorithmic strategy to apply generalized
linear models (GLMs) to this massive tensor data in which one set of variables
is associated with locations. Our method begins by fitting GLMs to each
location separately, and then builds an ensemble by blending information across
locations through regularization with what we term an aggregating penalty. Our
so called, Local-Aggregate Model, can be fit in a completely distributed manner
over the locations using an Alternating Direction Method of Multipliers (ADMM)
strategy, and thus greatly reduces the computational burden. Furthermore, we
propose to select the appropriate model through a novel sequence of faster
algorithmic solutions that is similar to regularization paths. We will
demonstrate both the computational and predictive modeling advantages of our
methods via simulations and an EEG classification problem.Comment: 41 pages, 5 figures and 3 table
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