Application Of Rough Granular Computing In Inverse System Method

In recent years, the relatively integrated design theory of inverse system method has been constructed for the general nonlinear system. However, as is known to all, this method is based on an accurate mathematical model of the controlled system. In many practical cases, however, the accurate mathematical description of the system is almost unknown. Even if the original system’s model is completely known in advance, the nonlinearities and complex coupling relationships contained in the model still make it quite hard to deduce the inversion.Firstly, the research actualities at home and abroad of rough set theory, granular computing and inverse model are summed up in the paper, whose relevant concepts, working processes and pivotal technologies are discussed. Moreover a rapid attribute reduction algorithms is proposed based on equivalence relation:Firstly, combining with granular computing and rough set theory, a heuristic algorithm based on knowledge granularity for attribute reduction is proposed. Analysis shows that the time complexity of this algorithm is smaller, and the experimental result proves that the proposed algorithm not only can reduce the decision table efficient but rapid.Using the advantage of granular computing theory in data processing, we can get the certain rules from the training data. Every rule represents a certain class of the batch of data. A inverse system model based on granular computing rules is present. After analyzing the characteristic of the radial basic function in this article, a method for constructing inverse system model based on granular and RBF networks is puts forward, and the process of modeling is introduced in detail. The simulating results verified the validity and superiority of this method.