Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A fuzzy classifier based on a four-layered feedforward neural network model is proposed. This connectionist fuzzy classifier, called CFC, realizes the ighted-Euclidean-distance fuzzy classification concept in a massively parallel manner to recognize input patterns. CFC employs a hybrid supervised/unsupervised learning scheme to organize referenced pattern vectors. This scheme not only overcomes the major drawbacks of multilayer perceptron models using the backpropagation algorithm, i.e., the local minimal problem and long training time, but also avoids the disadvantage of the huge storage space requirement of the probabilistic neural network. According to experimental results, CFC shows better accuracy for speech recognition than several existing methods, even in a noisy environment. Moreover, it has higher stability of recognition rates for different environmental conditions. A massively parallel hardware architecture has been developed to implement CFC. A bus-oriented multiprocessor, systolic processor structure, and pipelining are used to obtain low-cost, high-performance fuzzy classification. >
['Yau-Hwang Kuo', 'Cheng-I Kao', 'Jiahn-Jung Chen']
I am writing a research paper on a idea A fuzzy neural network model and its hardware implementation
[{"from": "human", "value": "I am writing a research paper on a idea A fuzzy neural network model and its hardware implementation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A fuzzy classifier based on a four-layered feedforward neural network model is proposed. This connectionist fuzzy classifier, called CFC, realizes the ighted-Euclidean-distance fuzzy classification concept in a massively parallel manner to recognize input patterns. CFC employs a hybrid supervised/unsupervised learning scheme to organize referenced pattern vectors. This scheme not only overcomes the major drawbacks of multilayer perceptron models using the backpropagation algorithm, i.e., the local minimal problem and long training time, but also avoids the disadvantage of the huge storage space requirement of the probabilistic neural network. According to experimental results, CFC shows better accuracy for speech recognition than several existing methods, even in a noisy environment. Moreover, it has higher stability of recognition rates for different environmental conditions. A massively parallel hardware architecture has been developed to implement CFC. A bus-oriented multiprocessor, systolic processor structure, and pipelining are used to obtain low-cost, high-performance fuzzy classification. >"}]