Applying the Fuzzy BWM to Determine the Cryptocurrency Trading System under Uncertain Decision Process

Abstract

The crypto and digital assets ecosystems have attracted investment, regulators, and speculators to their environment. As the blockchain-based framework can reduce transaction costs, generate distributed trust, and enable decentralized platforms, it has become a potential new base for decentralized business models. Previous studies have highlighted the advantages and drawbacks of each platform, such as interest rates, cost concerns, transparency issues, hacking issues, and hazards. Consequently, it is challenging for investors to evaluate the cryptocurrency trading system which determines the optimum exchanges and crucial aspects. Therefore, in order to rank the optimal digital token trading system, this paper develops an evaluation architecture to determine the various token trading systems. The developed architecture integrates fuzzy theory and the best-worst method (BWM) into the decision-making process to assess decision behaviors regarding preference for digital token trading systems in investors in Taiwan. First, this work establishes the views and parameters by modifying the Delphi method based on a literature review and survey. Second, the fuzzy-BWM is applied to obtain the fuzzy weights of the views and parameters. Then, defuzzification and BWM are used to rank the optimal alternatives of the digital token trading systems for investors. The results indicate that the optimal digital token trading system is the decentralized platform, and the critical parameters are gas fees, interest rates, and the mechanism of savings under fuzzy uncertain scenarios. This means that when considering the uncertain and ambiguous characteristics of the expert decision process in digital token trading systems, the evaluation is decentralized and the gas fees are the most important parameter in the digital token investment platform. Academically, the fuzzy BWM-based decision-making architecture can provide corporations and investors with valuable guidance to rank the optimal digital token trading systems based on fuzzy uncertain scenarios. Commercially, the proposed architecture could provide corporations and investors with a useful model to measure the optimal digital token trading system.

Equivalent-Input-Disturbance Based Robust Control Design for Fuzzy Semi-Markovian Jump Systems via the Proportional-Integral Observer Approach

“…In this study, it is assumed that the state variables in (4), which represents the measured output £(t), are unavailable. In order to estimate the measured output, the system in (4) is manually replicated with the same behavior to form the estimated output £(t) in (5). In particular, while replicating the actual system, a negligible error component…”

“…Furthermore, we define the error between the state (2) and observer (5) with the configuration ∆ℵ(t) = ℵ(t) − ‫(א‬t). Then, the corresponding error system can be written as:…”

“…The study based on the Takagi-Sugeno (T-S) fuzzy model technique has attracted a lot of attention due to its remarkable ability to approximate complex nonlinear systems as the weighted sum of linearized subsystems at operating points using fuzzy membership functions and if-then rules. In recent decades, remarkable research attention has been given to the stability and stabilization for T-S fuzzy systems [1][2][3][4][5][6]. In [7,8], the authors looked into the H ∞ filtering problem for nonlinear switched systems with time-varying delay by incorporating T-S fuzzy model characteristics.…”