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Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic...
The work presented in this paper concerns the detection of drowsy driving based on time series measurements of driving behavior. Artificial neural networks, trained using particle swarm optimization, have been used to combine several indicators of drowsy driving based on a data set originating from a large study carried out in the driving simulator at the Swedish National Road and Transportation Institute...
This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural...
This paper investigates the possibility of a pseudo-online adaptive training schema for Mamdani-type neuro-fuzzy models that have robust linguistic interpretability. As such verbatim models are incapable of complex constructs available to Takagi-Sugeno-type neuro-fuzzy models, a heuristic approach is developed to allow the rule bases to adapt accordingly to fundamental shifts in the characteristics...
Generative topographic mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal with noni. i.d. data such as multivariate time series in a variant called GTM through time (GTM-TT), defined as a constrained hidden Markov model (HMM). In this paper, we provide...
Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition of a similarity measure, with the condition that kernels are positive semi-definite (PSD). An alternative approach which places no such restrictions on the similarity measure is to construct a set of inputs and let each example...
Despite the success of its applications in many areas, the dynamic time warping (DTW) distance does not satisfy the triangle inequality (subadditivity). Once we have a subadditive distance measure for time series, the measure will have at least one significant advantage over DTW; one can directly plug such distance measure into systems which exploit the subadditivity to perform faster similarity search...
The recently developed methods in nonlinear dynamics and chaos time series analysis are used in this study to analyze, delineate and quantify the underlying coastal water level and surge dynamics in the North Sea along several locations at the Dutch coast. This study analyzes seven water level and surge data sets, five of which characterize coastal locations and two relate to the open sea locations...
In this paper a new method to resolve the overfitting problem for predicting complex systemspsila behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural networks and using simulated annealing for the optimization of their generalization. The major work is done based on the idea of ensemble neural networks...
Distributed intelligent systems like self-organizing wireless sensor and actuator networks are supposed to work mostly autonomous even under changing environmental conditions. This requires robust and efficient self-learning capabilities implementable on embedded systems with limited memory and computational power. We present a new solution called spiral recurrent neural networks with an online learning...
A new methodology for forecasting of time series is proposed. It is based on combination of two techniques: fuzzy transform and perception-based logical deduction on the basis of learned linguistic description.
Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering...
This paper presents a novel support vector regression (SVR) network for financial time series prediction. The SVR network consists of two layers of SVR: transformation layer and prediction layer. The SVRs in the transformation layer forms a modular network; but distinguished with conventional modular networks, the partition of the SVR modular network is based on the output domain that has much smaller...
This paper proposes a combination of methodologies based on a recent development -called Extreme Learning Machine (ELM)- decreasing drastically the training time of nonlinear models. Variable selection is beforehand performed on the original dataset, using the Partial Least Squares (PLS) and a projection based on Nonparametric Noise Estimation (NNE), to ensure proper results by the ELM method. Then,...
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