Reducing computational overhead by improving the CRI and IRI implication step

In conventional siso fuzzy expert systems (n-element input,m-element output),the implication step requires the o(n×m) operations using compositional rule-based inference (cri) and individual rule-based inference (iri). however,this introduces excessive complexity. this paper proposes two methods,sort compositional rule-based inference (scri) and sort individual rule-based inference (siri) aiming at reducing both temporal and spatial complexity by changing the operation of the implication step to o((n+m)log2(n+m)). we also propose a divide-and-conquer technique,called quicksort,to verify the accuracy of scri and siri algorithms deployment to easily outperform the cri and iri methods. © 2015 thoai phu vo and joy iong-zong chen.