An Improved KNN Algorithm of Intelligent Built-in Test

Aimed at the faults of K-nearest neighbor (KNN) algorithm in complex equipment´s built-in test (BIT), an improved KNN (IKNN) algorithm is proposed to solve the problem from two aspects. Firstly, the weight of each input feature is learned using neural network to make important features contribute more in the classifications; this improves the precision of classification. Secondly, clustering each sample of the training set to reduce the data volume of training set, this improves the running speed of the algorithm. Simulation experiments prove the effectiveness of the IKNN algorithm with higher precision and less calculation.