An integrated neural network algorithm for optimum performance assessment of auto industry with multiple outputs and corrupted data and noise
MetadataShow full item record
© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
In the real world encountering with noisy and corrupted data is unavoidable. Auto industry sector (AIS) as a one of the significant industry encounters with noisy and corrupted data regarding to its rapid development. Therefore, developing the performance assessment in this situation is so helpful for this industry. As Data envelopment Analysis (DEA) could not deal with noisy and corrupted data, the alternative method(s) is very important. As one of excellent and promising feature of artificial neural networks (ANNs) are theirs flexibility and robustness in noisy situation, they are a good alternative. This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques for efficiency assessment in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores of auto industry in various countries, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of AIS on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Another feature of proposed algorithm is its ability to calculate efficiency for multiple outputs. An example using real data is presented for illustrative purposes. In the application to the auto industries, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. To test the robustness of the efficiency results of the proposed method, the ability of proposed ANN algorithm in dealing with noisy and corrupted data is compared with Data Envelopment Analysis (DEA). Results of the robustness check show that the proposed algorithm is much more robust to the noise and corruption in input data than DEA.
Showing items related by title, author, creator and subject.
Distributed semi-supervised learning algorithms for random vector functional-link networks with distributed data splitting across samples and featuresXie, J.; Liu, S.; Dai, H.; Rong, Yue (2020)In this paper, we propose two manifold regularization (MR) based distributed semi-supervised learning (DSSL) algorithms using the random vector functional link (RVFL) network and alternating direction method of multipliers ...
Lam, H.; Ekong, U.; Liu, H.; Xiao, B.; Araujo, H.; Ling, S.H.; Chan, Kit Yan (2014)In this paper, the performance of the commonly used neural-network-based classifiers is investigated on solving a classification problem which aims to identify the object nature based on surface features of the object. ...
Pham, DucSon; Venkatesh, S. (2013)Compressed sensing (CS) is an important theory for sub-Nyquist sampling and recovery of compressible data. Recently, it has been extended to cope with the case where corruption to the CS data is modelled as impulsive ...