Electric appliance load type intelligent identification method and device
A load type, intelligent recognition technology, applied in the direction of measuring device, character and pattern recognition, and pattern recognition in signals, etc., can solve the problems of incomplete accurate recognition, single identification method, single characteristic nature, etc., to achieve rich feature information, Simple acquisition method and high recognition accuracy
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Embodiment 2
[0085] Example 2 selects the NBC classifier as the auxiliary classifier. Naive Bayes classification is defined as follows:
[0086] ⑴ Let x={a 1 ,a 2 ,...,a m} is an item to be classified, and each a is a characteristic attribute of x;
[0087] ⑵There is a category set C={y 1 ,y 2 ,...,y n};
[0088] (3) Calculate P(y 1 |x),P(y 2 |x),...,P(y n |x);
[0089] ⑷If P(y k |x)=max{P(y 1 |x),P(y 2 |x),...,P(y n |x)}, then x∈y k .
[0090] The specific method of calculating each conditional probability in step (3) is:
[0091] ① Find a set of items to be classified with known classification as the training sample set;
[0092] ②Statistically obtain the conditional probability estimates of each feature attribute under each category;
[0093] P(a 1 |y 1 ),P(a 2 |y 1 ),…,P(a m |y 1 );
[0094] P(a 1 |y 2 ),P(a 2 |y 2 ),…,P(a m |y 2 );
[0095] ...;
[0096] P(a 1 |y n ),P(a 2 |y n ),…,P(a m |y n ).
[0097] ③According to Bayes' theorem, there are: ...
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