this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

The ordered weighted averaging (OWA) operator uses the weights assigned to the ordered values rather than to the specific criteria. This allows one to model various aggregation preferences, preserving simultaneously the impartiality (neutrality) with respect to the individual attributes. The determination of ordered weighted averaging (OWA) operator weights is a crucial issue of applying the OW...

The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio (SNR). proposed method utilizes robust measures of location i.e., “Median” as well variation “Median absolute deviation (MAD) and Interquartile range (IQR)” SNR. By this way, two independent signal-to-noise ratios have been proposed. selects most informative gen...

Least absolute deviation (LAD) regression is an important tool used in numerous applications throughout science and engineering, mainly due to the intrinsic robust characteristics of LAD. In this paper, we show that the optimization needed to solve the LAD regression problem can be viewed as a sequence of maximum likelihood estimates (MLE) of location. The derived algorithm reduces to an iterat...

In this paper, we consider the problem of incorporating a wide set of real-world trading constraints to the meanvariance portfolio framework. Instead of using the mean-variance model directly, we use the equivalent Mean-Absolute Deviation (MAD) linear programming formulation. The addition of the trading constraints transforms the MAD model to a mixed-integer linear programming problem. We solve...

Abstract In this paper, we present a new multiperiod portfolio selection model with maximum absolute deviation model. The investor is assumed to seeks an investment strategy to maximize his/her terminal wealth, and minimize the total risk in all periods. Different with original consideration that risk is defined as the variance of terminal wealth, in our paper, the total risk is defined as the ...

We introduce a cardinality constrained multi-objective optimization problem for generating efficient portfolios within a fuzzy mean-absolute deviation framework. We assume that the return on a given portfolio is modeled by means of LR-type fuzzy variables, whose credibility distributions collect the contemporary relationships among the returns on individual assets. To consider credibility measu...

In this paper, the l1 norm penalty on the filter coefficients is incorporated in the least mean absolute deviation (LAD) algorithm to improve the performance of the LAD algorithm. The performance of LAD, zero-attracting LAD (ZA-LAD) and reweighted zero-attracting LAD (RZA-LAD) are evaluated for linear time varying system identification under the non-Gaussian (α-stable) noise environments. Effec...

It is well known that the least absolute deviation (LAD) estimators are more robust than the least squares estimators particularly in presence of heavy tail errors. We consider the LAD estimators of the unknown parameters of one dimensional chirp signal model under independent and identically distributed error structure. The proposed estimators are strongly consistent and it is observed that th...