Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a structure evolving learning method for fuzzy systems. The mechanism of the algorithm is that it is an error-reducing driven learning method. The proposed algorithm starts with a simple fuzzy system and evolves the system structure by adding more fuzzy terms and rules to reduce the model errors in a ‘greedy’ way. The main features of the proposed algorithm are summarized as three points. Firstly, it can automatically determine and control the number and location of fuzzy terms by following the error-reducing driven evolving process to achieve the desired accuracy. Secondly, it adds new fuzzy terms and rules by evenly distributing error to each sub-region aiming at an efficient set of fuzzy rules. Thirdly, it uses triangular membership functions with regular partitions and leads to fuzzy system models with good transparency and interpretability. Three benchmark examples are given to illustrate the advantages of the proposed algorithm.
['Di Wang', 'Xiao-Jun Zeng', 'John A. Keane']
I am writing a research paper on a idea A structure evolving learning method for fuzzy systems
[{"from": "human", "value": "I am writing a research paper on a idea A structure evolving learning method for fuzzy systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a structure evolving learning method for fuzzy systems. The mechanism of the algorithm is that it is an error-reducing driven learning method. The proposed algorithm starts with a simple fuzzy system and evolves the system structure by adding more fuzzy terms and rules to reduce the model errors in a \u2018greedy\u2019 way. The main features of the proposed algorithm are summarized as three points. Firstly, it can automatically determine and control the number and location of fuzzy terms by following the error-reducing driven evolving process to achieve the desired accuracy. Secondly, it adds new fuzzy terms and rules by evenly distributing error to each sub-region aiming at an efficient set of fuzzy rules. Thirdly, it uses triangular membership functions with regular partitions and leads to fuzzy system models with good transparency and interpretability. Three benchmark examples are given to illustrate the advantages of the proposed algorithm."}]