Application of a Novel PNN Evaluation Algorithm to a Greenhouse Monitoring System

Guan, Shouping; Fang, Qiuyang; Guan, Tianyi

doi:10.1109/tim.2021.3079558

Cited by 8 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Intelligent search algorithms mainly include the genetic algorithm [22], PSO [23] and differential evolution algorithm [24]. The neural networks algorithm can be classified into BP [25], RBF [26], PNN [27] and GRNN [28] according to their excitation functions. Compared with other methods, parameter self-adaptation based on fuzzy algorithm has the advantages of low computational load and strong robustness, which is suitable for online implementation in an embedded controller [29].…”

Section: Fuzzy-based Stanley Modelmentioning

confidence: 99%

The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model

et al. 2022

View full text Add to dashboard Cite

The unmanned operation of agriculture machinery in the full field of farmland is an important part of unmanned farm and smart agriculture. Although the autonomous navigation for agriculture robot has been widely studied in literature, research on the full-field path tracking problem of agriculture machinery is rare. In this paper, in order to enhance the adaptivity of path tracking algorithm, an improved fuzzy Stanley model (SM) is proposed based on particle swarm optimization (PSO), where the control gain is modified adaptively according to the tracking error, velocity and steering actuator saturation. The PSO-enhanced fuzzy SM (PSO-FSM) is verified by experiments on numerical simulation and self-driving of mobile vehicle . Simulation results indicate that the PSO-FSM achieves a better result than SM and FSM, where PSO-FSM changes the control gain adaptively under different velocities and actuator saturation conditions, and the maximum lateral errors of SM and PSO-FSM for mobile vehicle autonomous turning are 0. 32 m and 0.03 m, respectively. When the location of the mobile vehicle deviates from the expected path at 4 m in a lateral direction, the distance of the guided trajectory for the mobile vehicle to reach the expected path is no more than 5 m. A preliminary experiment is also carried out for a wheeled combine harvester working on slippery soil, and the result indicates that the maximum lateral tracking error of PSO-FSM is 0.63 m, which is acceptable for the path tracking of a combine harvester with a large operation width.

show abstract

Section: Fuzzy-based Stanley Modelmentioning

confidence: 99%

The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The input layer is 𝓍 object which consists of some 𝑘 lengths features vector and classified to 𝑛 class (Guan et al, 2021). Processes after input layer executed are:…”

Section: Pnnmentioning

confidence: 99%

Classification of Burned Peatland Using Probabilistic Neural Network Algorithm Based on High Temporal Data

Feta

2022

J. Ris. Inform.

View full text Add to dashboard Cite

Forest or land fires in Indonesia do not only occur in drylands but also in peatlands. Peatland fires are more dangerous and more difficult to overcome compared to non-peatland fires and the impacts of peatland fires are very harmful to society. One of the solutions in assessing forest and peatland fires is remote sensing technology. Satellite images obtained from remote sensing technology are usually classified for further analysis. The main objective of this study is to develop a classification model using Probabilistic Neural Network (PNN) to classify areas in peatland before, during, and after being burned on the satellite image Landsat 7 ETM +. Furthermore, the model is used to get the trajectory pattern of the burned area using the DBScan algorithm. The study area is Ogan Komering Ilir District, South Sumatera Province, image Landsat 7 ETM + taken from January 2015 – December 2015.

show abstract

Design of Remote Intelligence Monitoring System Based on LabVIEW for the Greenhouse

2023

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

Application of a Novel PNN Evaluation Algorithm to a Greenhouse Monitoring System

Cited by 8 publications

References 36 publications

The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model

The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model

Classification of Burned Peatland Using Probabilistic Neural Network Algorithm Based on High Temporal Data

Design of Remote Intelligence Monitoring System Based on LabVIEW for the Greenhouse

Contact Info

Product

Resources

About