The main objective of this paper is to shed light on this new but highly promising hybrid approach of fuzzy logic and adaptive neuro-fuzzy network, its use in control systems, particularly in the field of computer science and engineering applications. The original objective of this paper is to study the variations in the energy consumption by changing the input parameters and the stability of the system in terms of system response time and error arising during changing the input values of the system. This paper takes the model a step forward, in that the mathematically based and derived model is modified to use in design a practical fuzzy controller and to apply it in a real-life application, such as the temperature control problem. This model is modified to make traditional controller some explicit when multiple constrained resources available. This model is derived to provide a robust quantified fuzzy system that can find out the level of satisfaction and provide a practical and intelligent tool for further accessing the impact of different options available to the fuzzy logic controller