Fuzzy rule generation apparatus for neuro circuit network system and method thereof
First Claim
1. A fuzzy rule generation apparatus for a neuro circuit network system, comprising:
- an input layer for receiving a plurality of data;
a hidden layer for judging whether said plurality of data inputted thereto through said input layer is within an effective radius of a fuzzy variable and for automatically generating a number of fuzzy rules using the Gausian function until each of said data is within said effective radius of one of said fuzzy rules;
a parameter block for storing a parameter learned by error back propagation so as to compute a fuzzy rule generated by said hidden layer; and
an output layer for computing the last defuzzy operation using a fuzzy rule value generated by the hidden layer and a fuzzy variable value of a parameter block necessary for a defuzzy operation.
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Abstract
An improved fuzzy rule generation apparatus for a neuro circuit network system and a method thereof capable of computing a fuzzy output and minimizing the number of fuzzy rules by automatically adjusting the number of fuzzy rules in accordance with a given problem and using general fuzzy function having a triangle shape or a trapezoid shape using a multi-parameter of a hidden layer and an output layer when computing a fuzzy output, which includes an input layer for receiving a plurality of data; a hidden layer for judging whether a plurality of data inputted thereto through the input layer is within an effective radius of a fuzzy variable and for automatically generating a fuzzy rule to an "n" number using the Gausian function in accordance with a result of the judgement; a parameter block for storing a parameter learned by an error back propagation learning so as to compute a fuzzy rule generated by the hidden layer; and an output layer for computing the last defuzzy operation using a fuzzy rule value generated by the hidden layer and a fuzzy variable value of a parameter block necessary for a defuzzy operation.
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Citations
10 Claims
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1. A fuzzy rule generation apparatus for a neuro circuit network system, comprising:
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an input layer for receiving a plurality of data; a hidden layer for judging whether said plurality of data inputted thereto through said input layer is within an effective radius of a fuzzy variable and for automatically generating a number of fuzzy rules using the Gausian function until each of said data is within said effective radius of one of said fuzzy rules; a parameter block for storing a parameter learned by error back propagation so as to compute a fuzzy rule generated by said hidden layer; and an output layer for computing the last defuzzy operation using a fuzzy rule value generated by the hidden layer and a fuzzy variable value of a parameter block necessary for a defuzzy operation. - View Dependent Claims (2)
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3. A fuzzy rule generation method for a neuro circuit network system, comprising the steps of:
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a first step which generates a first hidden neuron by receiving data through an input layer, sets one of said data as a center value, and sets a reference deviation value and an initial value of a parameter of a fuzzy variable in accordance with a fuzzy variable to be used; a second step which judges whether the next data received through the input layer corresponds to a first hidden neuron, generates a new hidden neuron when the next data does not correspond to the first hidden neuron, and sets another parameter; a third step which repeatedly performs said second step until each of said data corresponds to one of said hidden neurons; a fourth step which learns a parameter through error back-propagation and judges whether a learned datum corresponds to one of the hidden neurons; a fifth step which generates a new neuron when the learned datum does not correspond to said one of the hidden neurons, and sets a parameter; and a sixth step which judges whether the number of learning iterations or errors reaches a predetermined level when the learned datum corresponds to said one of the hidden neurons, and if not, repeats said fourth step and if so, generates a defuzzy operation value by computing the hidden neuron and the predetermined parameter value which were generated in the second step and the fourth step. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10)
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Specification