5,658 research outputs found
Time series forecasting using a TSK fuzzy system tuned with simulated annealing
In this paper, a combination of a Takagi-Sugeno fuzzy system (TSK) and simulated annealing is used to predict well known time series by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules. The results of the proposed method are encouraging indicating that simulated annealing and fuzzy logic are able to combine well in time series prediction
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
A novel hybrid data-driven approach is developed for forecasting power system
parameters with the goal of increasing the efficiency of short-term forecasting
studies for non-stationary time-series. The proposed approach is based on mode
decomposition and a feature analysis of initial retrospective data using the
Hilbert-Huang transform and machine learning algorithms. The random forests and
gradient boosting trees learning techniques were examined. The decision tree
techniques were used to rank the importance of variables employed in the
forecasting models. The Mean Decrease Gini index is employed as an impurity
function. The resulting hybrid forecasting models employ the radial basis
function neural network and support vector regression. Apart from introduction
and references the paper is organized as follows. The section 2 presents the
background and the review of several approaches for short-term forecasting of
power system parameters. In the third section a hybrid machine learning-based
algorithm using Hilbert-Huang transform is developed for short-term forecasting
of power system parameters. Fourth section describes the decision tree learning
algorithms used for the issue of variables importance. Finally in section six
the experimental results in the following electric power problems are
presented: active power flow forecasting, electricity price forecasting and for
the wind speed and direction forecasting
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network
In this study, an artificial neural network (ANN) based on particle swarm
optimization (PSO) was developed for the time series prediction. The hybrid
ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the
short-term . The performance prediction was evaluated and compared with
another studies available in the literature. Also, we presented properties of
the dynamical system via the study of chaotic behaviour obtained from the
predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with
a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in
order to obtain a new estimator of the predictions, which also allowed us to
compute uncertainties of predictions for noisy Mackey--Glass chaotic time
series. Thus, we studied the impact of noise for several cases with a white
noise level () from 0.01 to 0.1.Comment: 11 pages, 8 figure
Modelling of Metallurgical Processes Using Chaos Theory and Hybrid Computational Intelligence
The main objective of the present work is to develop a framework for modelling and controlling of a real world multi-input and multi-output (MIMO) continuously drifting metallurgical process, which is shown to be a complex system. A small change in the properties of the charge composition may lead to entirely different outcome of the process. The newly emerging paradigm of soft-computing or Hybrid Computational Intelligence Systems approach which is based on neural networks, fuzzy sets, genetic algorithms and chaos theory has been applied to tackle this problem In this framework first a feed-forward neuro-model has been developed based on the data collected from a working Submerged Arc Furnace (SAF). Then the process is analysed for the existence of the chaos with the chaos theory (calculating indices like embedding dimension, Lyapunov exponent etc). After that an effort is made to evolve a fuzzy logic controller for the dynamical process using combination of genetic algorithms and the neural networks based forward model to predict the system’s behaviour or conditions in advance and to further suggest modifications to be made to achieve the desired results
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
Time series prediction by perturbed fuzzy model
This paper presents a fuzzy system
approach to the prediction of nonlinear
time series and dynamical systems based
on a fuzzy model that includes its
derivative information. The underlying
mechanism governing the time series,
expressed as a set of IF–THEN rules, is
discovered by a modified structure of fuzzy
system in order to capture the temporal
series and its temporal derivative information.
The task of predicting the future is
carried out by a fuzzy predictor on the
basis of the extracted rules and by the
Taylor ODE solver method. We have
applied the approach to the benchmark
Mackey-Glass chaotic time series.This work was supported by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) under grant POSI/SRI/41975/2001
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