Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Han, Yang; Liu, Chunbao; Yan, Lingyun; Ren, Lei

doi:10.3390/s21020526

Cited by 18 publications

(4 citation statements)

References 35 publications

(53 reference statements)

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“…Each neuron cell is composed of an axon and a plurality of dendrites, and each neuron cell is connected with axons of a plurality of neuron cells through a plurality of dendrites and used for receiving stimulation signals. e artificial neural network (ANN) is a nonlinear dynamic system with good adaptability and wide application range [31][32][33][34][35][36][37], especially in pattern recognition.…”

Section: E Proposed Aco-bpnn Modelmentioning

confidence: 99%

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

Zhu

2021

Computational Intelligence and Neuroscience

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The basic idea of face recognition technology is to compare the matching degree between the standard face image marked with identity information and the static or dynamic face collected from the actual scene, which includes two main research contents: face feature extraction and face feature recognition. Traditional identification generally proves who we are through certificates, passwords, or certificates plus passwords. With the development of science and technology, face recognition technology will occupy an increasingly important position. Inspired by the human brain, the artificial neural network (ANN) is an information extraction system based on imitating the basic function and structure of the human brain and abstracted by physical and mathematical research methods. Based on the traditional BP neural network model, this paper proposes an ant colony algorithm-enabled BP neural network (ACO-BPNN) model and applies it to face recognition. Experimental results show that, similar to other face recognition techniques, the facial feature location needs to adapt to various changes of faces to the maximum extent, so the recognition and classification effect of the whole face feature extracted from the whole face image on the changes of such partial areas is not good, while the local feature extraction method based on ACO-BPNN can achieve a good recognition and classification effect.

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Section: E Proposed Aco-bpnn Modelmentioning

confidence: 99%

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

Zhu

2021

Computational Intelligence and Neuroscience

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“…The accuracy of the iris data set reached 97.8%, achieving the highest accuracy (Hu et al, 2020). Han et al also optimized and improved the decision tree algorithm in this study, introduced temporal feature selection, combined it with the decision tree algorithm, used ant miner fuzzy decision tree classifier to extract intelligent fuzzy rules from weighted temporal capabilities, and then used fuzzy rule extractor to reduce the diversity of functions in the extracted rules (Han et al, 2021). Choi et al designed a new decision tree structure.…”

Section: Literature Reviewmentioning

confidence: 85%

The Promotion of Women's Leisure Sports Behavior Based on Improved Decision Tree Algorithm

Luo

2023

International Journal of Intelligent Information Technologies

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In women's daily leisure choices, sports is an important content that cannot be ignored. In this context, this paper studies the promotion of women's leisure sports behavior based on improved decision tree algorithm. Based on the simple analysis of the research progress of leisure sports and decision tree algorithm, a female leisure sports behavior model based on decision tree is constructed. Based on the decision tree algorithm, the calculation method of information gain rate is optimized to avoid logarithmic operation, and the continuous attributes are discretized. Simulation results show that in terms of classification accuracy, the improved decision tree algorithm is significantly higher than the classical decision tree algorithm, and can significantly shorten the running time, which has high application value in the realization of accurate classification analysis of female leisure sports behavior.

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“…Neural network construction mapper is to build mapper through neural network. When solving problems with the same accuracy requirements, the structure of BPNN is simpler than other neural networks (Han et al , 2021). Moreover, BPNN has strong nonlinear mapping ability and can realize a mapping function from input to output.…”

Section: Design and Training Of Neural Network Mappermentioning

confidence: 99%

A CPG-based gait planning and motion performance analysis for quadruped robot

Wei

Liu

et al. 2022

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Purpose To achieve stable gait planning and enhance the motion performance of quadruped robot, this paper aims to propose a motion control strategy based on central pattern generator (CPG) and back-propagation neural network (BPNN). Design/methodology/approach First, the Kuramoto phase oscillator is used to construct the CPG network model, and a piecewise continuous phase difference matrix is designed to optimize the duty cycle of walk gait, so as to realize the gait planning and smooth switching. Second, the mapper between CPG output and joint drive is established based on BP neural network, so that the quadruped robot based on CPG control has better foot trajectory to enhance the motion performance. Finally, to obtain better mapping effect, an evaluation function is resigned to evaluate the proximity between the actual foot trajectory and the ideal foot trajectory. Genetic algorithm and particle swarm optimization are used to optimize the initial weights and thresholds of BPNN to obtain more accurate foot trajectory. Findings The method provides a solution for the smooth gait switching and foot trajectory of the robot. The quintic polynomial trajectory is selected to testify the validity and practicability of the method through simulation and prototype experiment. Originality/value The paper solved the incorrect duty cycle under the walk gait of CPG network constructed by Kuramoto phase oscillator, and made the robot have a better foot trajectory by mapper to enhance its motion performance.

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Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Cited by 18 publications

References 35 publications

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

[Retracted] A Face Recognition System Using ACO‐BPNN Model for Optimizing the Teaching Management System

The Promotion of Women's Leisure Sports Behavior Based on Improved Decision Tree Algorithm

A CPG-based gait planning and motion performance analysis for quadruped robot

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