Fuzzy systems and networks are vital within the armoury of fuzzy tools and applicable to real life decision making environments. Three types of fuzzy systems introduced in literatures which are systems with single rule base, systems with multiple rule bases and system with networked rule bases. This research introduces novel extension of the Technique of Ordering of Preference by Similarity to Ideal Solution (TOPSIS) methods and uses fuzzy systems and networks to solve multi-criteria decision making problems where both benefit and cost are presented as subsystems. In conjunction, the implementation of fuzzy sets type-1, type-2 and Z-number of proposed approaches is also presented. Furthermore, literatures have observed that tracking the performance of criteria is crucial by controlling the estimation of uncertainty of the criteria. Thus, the decision maker evaluates the performance of each alternative and further observes the performance for both benefit and cost criteria. This research improves significantly the transparency of the TOPSIS methods while ensuring higher effectiveness in comparison to established approaches. Ensuring the practicality and the effectiveness of proposed methods in a realistic scenario, the problem of ranking traded stock is studied. This case study is conducted based on stocks traded in a developing financial market such as Kuala Lumpur Stock Exchange. The ranking based on proposed methods is validated comparatively using performance indicators such as Spearman Rho correlation, Kendall Tau correlation, Root Mean Square Errror and Average Absolute Distance by assuming ranking based on return on investment as a benchmarking. Based on the case study, the proposed methods outperform the established TOPSIS methods in term of average rank position.