We drop-shipped a novel Round Robin Neuro-Fuzzy System (RRNFS) model for the decision makers, based on neuro-fuzzy system for the processing of scheduling in a batch operating system. TS fuzzy model (Takagi & Sugeno, 1985) implemented in the RRNFS proposed model to identify the ideal Time Quantum. Our proposed RRNFS model takes two inputs: the total number of processes and the average burst time (ABT) of each process that is presented in the ready queue, fuzzifying the input values, activate the necessary rules of the proposed neuro fuzzy controller, and then determines the best Time Quantum for each process in the ready queue. Once Time quantum is calculated, every process will run on central processing unit as per the allocated Time quantum reduces the context switching, turnaround and waiting time. We bespoke the performance of the proposed model over date sets and compared our results with the classical round robin policy and modified round robin using fuzzy logic scheduling algorithms. We developed the neuro fuzzy technic for Xavier Normal function to minimize the error of the proposed model with the targeted time quantum.

Neuro-fuzzy system (NFS), Time Quantum (TQ), Round robin (RR) scheduling, Ready queue (RQ), Xavier Normal function.

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