Hybrid Metric K-Nearest Neighbor Algorithm and Applications

Zhongyuan Liang,Luis Payá,Mei Liu,Chao Zhang,Peisi Zhong,Qingjun Song,Xiao Wang

doi:10.1155/2022/8212546

Abstract

The K-Nearest Neighbor (KNN) algorithm is a classical machine learning algorithm. Most KNN algorithms are based on a single metric and do not further distinguish between repeated values in the range of K values, which can lead to a reduced classification effect and thus affect the accuracy of fault diagnosis. In this paper, a hybrid metric-based KNN algorithm is proposed to calculate a composite metric containing distance and direction information between test samples, which improves the discriminability of the samples. In the experiments, the hybrid metric KNN (HM-KNN) algorithm proposed in this paper is compared and validated with a variety of KNN algorithms based on a single distance metric on six data sets, and an HM-KNN application method is given for the forward gait stability control of a bipedal robot, where the abnormal motion is considered as a fault, and the distribution of zero moment points when the abnormal motion is generated is compared. The experimental results show that the algorithm has good data differentiation and generalization ability for different data sets, and it is feasible to apply it to the walking stability control of bipedal robots based on deep neural network control.

Highlights

K-Nearest Neighbor (KNN) algorithm is a classical nonparametric machine learning classification algorithm. e trained KNN model is usually used as a fault classifier in engineering
Many scholars have applied deep learning techniques to the field of fault detection and diagnosis (FDD) [10, 11], deep neural networks are sensitive to hyperparameters, and for different hyperparameters, the model classification effect can vary dramatically, so the KNN algorithm, which does not depend on the initial hyperparameters, is a more desirable alternative algorithm. erefore, the KNN algorithm is widely used in fault prediction [12,13,14] and fault diagnosis [15,16,17,18,19,20]
The optimized hybrid metric KNN (HM-KNN) algorithm is used in the bipedal robot forward gait walking task

Summary

Introduction

KNN algorithm is a classical nonparametric machine learning classification algorithm. e trained KNN model is usually used as a fault classifier in engineering. Reliable KNN classification models can provide accurate fault detection and diagnosis (FDD) information. Model-based approaches must construct a system model to describe real processes and perform fault detection and diagnosis by analyzing redundancies. FDD for robots in performing tasks requires fast, online, and small computational effort, and machine learning algorithms are well suited to meet the needs of robotic FDD tasks. Both traditional machine learning-based classification and deep neural network classification are typical data-driven FDD approaches. Many scholars have applied deep learning techniques to the field of FDD [10, 11], deep neural networks are sensitive to hyperparameters, and for different hyperparameters, the model classification effect can vary dramatically, so the KNN algorithm, which does not depend on the initial hyperparameters, is a more desirable alternative algorithm. erefore, the KNN algorithm is widely used in fault prediction [12,13,14] and fault diagnosis [15,16,17,18,19,20]

Methods

Results

Conclusion