The Research Of Fuzzy Interpolative Reasoning Method Based On The Sparse Fuzzy Rule Bases

When rule base is sparse, any reasoning result can not be gotten by traditional CRI method, however interpolative reasoning methods can tackle this problem. Now, there are some interpolative reasoning methods, but they may include complex computation and can't keep the convexity of the reasoning consequence. So, the target of this dissertation is the research on how to improve the customary reasoning, and proposed a new sparse fuzzy reasoning method.Firstly, the interpolative reasoning method based on center of Gravity was described. But, this method didn't allow the conditions appearing in the antecedent part of rule to be represented by trapezoidal fuzzy numbers, so this paper generalized the method. The generalized fuzzy interpolative method based on center of gravity allows the conditions appearing in the antecedent part and the consequence of the rules to be represented not only by triangular fuzzy numbers but also by trapezoidal fuzzy numbers.Secondly, by analyzed and simulated, it was found that the fuzzy lagrange's interpolative reasoning method can't guarantee the reasoning consequences are normal and convex. So a fuzzy lagrange's interpolative method based on geometric parameter was presented. Reasoning is simple by the method; moreover it can keep the convexity of the reasoning consequence.Finally, a fuzzy interpolative reasoning method based on core was proposed. When the fuzzy rule bases are sparse, the reasoning conclusions can be derived simply and are normal convex fuzzy sets. By this new method the new reasoning method is programmed in MATLAB subsequently. The one dimension sparse fuzzy reasoning method was expanded into multi-dimension fuzzy reasoning, and its application area was spread.