Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Simplified fuzzy ARTMAP (SFAM) is a simplification of fuzzy ARTMAP (FAM) by reducing the computational overhead and architectural redundancy. SFAM is porful neural network in the application of prediction and classifier. Individual SFAM performance depends on the ordering of training sample presentation. A multiple classifier combination scheme is proposed in this paper to obtain reliable and accurate fault diagnosis. SFAMs input the data vectors in the complement code format and output fault diagnosis results. The sum rule voting algorithm combines the SFAMs outputs and generates final conclusion. A ight is assigned to each SFAM according to its historical achievements. Case study has shown that the proposed method is effective in fault diagnosis application.
['Zhiyong Tang', 'Xiang_an Yan']
I am writing a research paper on a idea Voting Algorithm of Fuzzy ARTMAP and Its Application to Fault Diagnosis
[{"from": "human", "value": "I am writing a research paper on a idea Voting Algorithm of Fuzzy ARTMAP and Its Application to Fault Diagnosis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Simplified fuzzy ARTMAP (SFAM) is a simplification of fuzzy ARTMAP (FAM) by reducing the computational overhead and architectural redundancy. SFAM is porful neural network in the application of prediction and classifier. Individual SFAM performance depends on the ordering of training sample presentation. A multiple classifier combination scheme is proposed in this paper to obtain reliable and accurate fault diagnosis. SFAMs input the data vectors in the complement code format and output fault diagnosis results. The sum rule voting algorithm combines the SFAMs outputs and generates final conclusion. A ight is assigned to each SFAM according to its historical achievements. Case study has shown that the proposed method is effective in fault diagnosis application."}]