isy: improved sugeno-yasukawa fuzzy modelling approach using a novel clustering and project method for input partitioning

Soft computing algorithm such as fuzzy logic, neural networks, and evolutionary algorithms are widely used in many fields. fuzzy logic, in particular, has gained significant popularity due to its significant ability in modelling. so far, various methods of fuzzy modelling have been presented; each of these methods has its advantages and disadvantages. while all methods start from the input, sugeno-yasukawa (sy) differs by initiating the analysis from the output. the popularity of the sy method can be attributed to its effective rule extraction algorithm, which employs a clustering process to determine input membership functions. in this paper, we propose a cluster search algorithm and a new fuzzy partitioning method that enhance the mapping of the output space to the input space by distributing gaussian functions for each data point within a cluster and calculating their membership values. with this proposed new clustering search method, the performance of the sy method is improved. through simulations, the proposed method has improved the mean square errors (mse) criterion by 0.001, and improved the accuracy criterion by 1.5 percent.

isy: improved sugeno-yasukawa fuzzy modelling approach using a novel clustering and project method for input partitioning

soft computing algorithm such as fuzzy logic, neural networks, and evolutionary algorithms are widely used in many fields. fuzzy logic, in particular, has gained significant popularity due to its significant ability in modelling. so far, various methods of fuzzy modelling have been presented; each of these methods has its advantages and disadvantages. while all methods start from the input, sugeno-yasukawa (sy) differs by initiating the analysis from the output. the popularity of the sy method can be attributed to its effective rule extraction algorithm, which employs a clustering process to determine input membership functions. in this paper, we propose a cluster search algorithm and a new fuzzy partitioning method that enhance the mapping of the output space to the input space by distributing gaussian functions for each data point within a cluster and calculating their membership values. with this proposed new clustering search method, the performance of the sy method is improved. through simulations, the proposed method has improved the mean square errors (mse) criterion by 0.001, and improved the accuracy criterion by 1.5 percent.