Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new subsethood-product fuzzy neural inference system (SuPFuNIS) is presented in this paper. It has the flexibility to handle both numeric and linguistic inputs simultaneously. Numeric inputs are fuzzified by input nodes which act as tunable feature fuzzifiers. Rule based knowledge is easily translated directly into a network architecture. Connections in the network are represented by Gaussian fuzzy sets. The novelty of the model lies in a combination of tunable input feature fuzzifiers; fuzzy mutual subsethood-based activation spread in the network; use of the product operator to compute the extent of firing of a rule; and a volume-defuzzification process to produce a numeric output. Supervised gradient descent is employed to train the centers and spreads of individual fuzzy connections. A subsethood-based method for rule generation from the trained network is also suggested. SuPFuNIS can be applied in a variety of application domains. The model has been tested on Mackey-Glass time series prediction, Iris data classification, Hepatitis medical diagnosis, and function approximation benchmark problems.  also use a standard truck backer-upper control problem to demonstrate how expert knowledge can be used to augment the network. The performance of SuPFuNIS compares excellently with other various existing models.
['Sandeep Paul', 'Satish Kumar']
I am writing a research paper on a idea Subsethood-product fuzzy neural inference system (SuPFuNIS)
[{"from": "human", "value": "I am writing a research paper on a idea Subsethood-product fuzzy neural inference system (SuPFuNIS)"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new subsethood-product fuzzy neural inference system (SuPFuNIS) is presented in this paper. It has the flexibility to handle both numeric and linguistic inputs simultaneously. Numeric inputs are fuzzified by input nodes which act as tunable feature fuzzifiers. Rule based knowledge is easily translated directly into a network architecture. Connections in the network are represented by Gaussian fuzzy sets. The novelty of the model lies in a combination of tunable input feature fuzzifiers; fuzzy mutual subsethood-based activation spread in the network; use of the product operator to compute the extent of firing of a rule; and a volume-defuzzification process to produce a numeric output. Supervised gradient descent is employed to train the centers and spreads of individual fuzzy connections. A subsethood-based method for rule generation from the trained network is also suggested. SuPFuNIS can be applied in a variety of application domains. The model has been tested on Mackey-Glass time series prediction, Iris data classification, Hepatitis medical diagnosis, and function approximation benchmark problems.  also use a standard truck backer-upper control problem to demonstrate how expert knowledge can be used to augment the network. The performance of SuPFuNIS compares excellently with other various existing models."}]