Simulation Study of Deep Belief Network-Based Rice Transplanter Navigation Deviation Pattern Identification and Adaptive Control DOI Creative Commons

Xianhao Duan,

Peng Fang,

Neng Xiong

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 790 - 790

Published: Jan. 15, 2025

The navigation field of agricultural machinery has entered the intelligent stage, but control performance paddy represented by rice transplanters is not stable in complex environments. Therefore, this study proposes a method to identify deviation patterns based on Deep Belief Network (DBN) and designs an adaptive preview distance driver model for each pattern. Among them, pattern identification two-stage algorithm. First, determine whether current status abnormal. Then, classification was refined different abnormal states. divided into two levels. main regulator calculates dynamic according state variable; sub-regulator adjustment value degree. In test method, all models show excellent stability accuracy, speed algorithm meets high frequency transplanter system. algorithm, compared with static distance, proposed can effectively suppress disturbance navigation.

Language: Английский

Artificial intelligence models for predicting unconfined compressive strength of mixed soil types: focusing on clay and sand DOI
Barada Prasad Sethy,

Umashankar Prajapati,

K Neelashetty

et al.

Asian Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Language: Английский

Citations

1
Simulation Study of Deep Belief Network-Based Rice Transplanter Navigation Deviation Pattern Identification and Adaptive Control DOI Creative Commons

Xianhao Duan,

Peng Fang,

Neng Xiong

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 790 - 790

Published: Jan. 15, 2025

The navigation field of agricultural machinery has entered the intelligent stage, but control performance paddy represented by rice transplanters is not stable in complex environments. Therefore, this study proposes a method to identify deviation patterns based on Deep Belief Network (DBN) and designs an adaptive preview distance driver model for each pattern. Among them, pattern identification two-stage algorithm. First, determine whether current status abnormal. Then, classification was refined different abnormal states. divided into two levels. main regulator calculates dynamic according state variable; sub-regulator adjustment value degree. In test method, all models show excellent stability accuracy, speed algorithm meets high frequency transplanter system. algorithm, compared with static distance, proposed can effectively suppress disturbance navigation.

Language: Английский

Citations

0