A new strategy in fuzzy inference systems and in AI: the selective rules activation (SRA) algorithm

In both crisp and fuzzy inference machines, the degree of parallelism required to yield one complete elementary inference, i.e., an inference for one node and one output variable, in one processing step is defined as the dimension of the inference. It is shown that the complexity of the hardware and the complexity of the computation can be substantially decreased by using a selective activation of the inference rules. The algorithm discussed allows the building of hierarchical selective fuzzy systems. The algorithm for selective rule activation is presented for a one-dimensional input space case, i.e., for a single input variable case. The algorithm can be quite easily implemented in hardware, such as a rule chip able to perform a greater number of rules