Abstract
Nowadays, the success of a company is dependent to the novelty of the company in developing new items. Product design and engineering are a basic phase in developing new commodities which examines the product economically and technologically. In the proposed study, “Trust” is identified as an effective factor on the life cycle of the new designed product. This study addresses a simulation structure to generate all the possible trust modes between two agents over time and implements four prediction methods to forecast the trust value of the new item. The time horizon is considered to be short term and middle term, and 27 and 108 scenarios are designed, respectively, based on three categories involving high, medium and short trust. Here, three prediction techniques: conventional time series, artificial neural networks and adaptive neuro-fuzzy inference system, are recommended and compared. By comparing MAPEs of all prediction methods, the best technique is identified.
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Notes
City-block metric: If a and b are vectors with m-dimension, the city-block metric has been defined to calculate distance between a and b according to the following formula:
$$ \mathop \sum \limits_{{{\text{i}} = 1}}^{\text{m}} \left| {b_{i} - a_{i} } \right| .$$(8)
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Azadeh, A., Sadri, S., Saberi, M. et al. An Integrated Fuzzy Trust Prediction Approach in Product Design and Engineering. Int. J. Fuzzy Syst. 19, 1190–1199 (2017). https://doi.org/10.1007/s40815-017-0314-1
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DOI: https://doi.org/10.1007/s40815-017-0314-1