Experiment-based affect heuristic using fuzzy rules and Taguchi statistical method for tuning complex systems

Affect heuristic is a subjective feeling that enables human?s quick and efficient decision-making in unstructured and uncertain environments. It is an experience-based subconscious process in which information is framed for quick interpretation. Here, we propose a hybrid Experiment-based Decision-making architecture based on Affect Heuristics (EDAH) that uses possibility and probability theories in combination utilizing fuzzy logic?s ability to address deterministic uncertainty and graduality of truth, along with Taguchi method?s ability to exploit the statistical data as a robust and suboptimal experiment design methodology. Accordingly, the proposed paradigm aims to make the best decision from a set of feasible decisions using fewer experiments by inserting reliable subjective information by the expert in the random samples of experiments. The proposed method is mathematically defined and then applied to the problem of Targeted Drug Delivery (TDD) for cancer treatment in the two states of with and without noise. Furthermore, we study the effect of three membership functions, consisting of triangular, trapezoidal, and Gaussian forms on our decisions? selection for making an improved medical decision and avoiding an unprofitable one by recognizing existing opportunities. Results indicate considerable improvement of normoxic and hypoxic cell densities when compared with the competing approaches.