Study on Prediction of the Total Power of Agricultural Machinery Based on Fuzzy BP Network

2015 ◽  
pp. 551-558 ◽  
Author(s):  
Hongfu Ai
Keyword(s):  
Total Power ◽  
Agricultural Machinery ◽  
Bp Network

scholarly journals BP Network Control for Resource Allocation and QoS Ensurance in UAV Cloud

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ang Gao ◽  
Yansu Hu ◽  
Lixin Li ◽  
Xu Li
Keyword(s):  
Real Time ◽  
Controller Design ◽  
Network Control ◽  
Total Power ◽  
Neuron Network ◽  
Heavy Load ◽  
Bp Network ◽  
Light Load ◽  
Total Power Consumption ◽  
Real Time Applications

Unmanned aerial vehicle (UAV) cloud can greatly enhance the intelligence of unmanned systems by dynamically unloading the compute-intensive applications to cloud. For the uncertain nature of UAV missions and fast-changing environment, different UAV applications may have different quality of service (QoS) requirements. This paper proposes a mixed QoS ensurance and energy-balanced (MQEB) architecture for UAV cloud from a view of control theory, which can support both hard and soft QoS ensurance with the consideration of energy saving. The hard and soft QoS requirements are decoupled by being normalized into a two-level cascaded feedback loop. The former is time slot loop (TS-Loop) to enforce the absolute QoS ensurance for real-time applications, and the latter is contention window loop (CW-Loop) to enforce the plastic QoS ensurance for non-real-time applications. Finally, the back propagating (BP) neuron network is used for parameters’ self-tuning and controller design. The hardware experiments demonstrate the feasibility of MQEB. In heavy load, MQEB has greater throughput and better energy efficiency, and in light load, MQBE has lower total power consumption.

Application of Generalized Regression Neural Network to the Agricultural Machinery Demand Forecasting

2013 ◽  
Vol 278-280 ◽  
pp. 2177-2182 ◽  
Author(s):  
Wei Luo ◽  
Zhuo Fu
Keyword(s):  
Neural Network ◽  
Demand Forecasting ◽  
Parameters Optimization ◽  
Small Samples ◽  
Total Power ◽  
Generalized Regression Neural Network ◽  
Agricultural Machinery ◽  
Generalized Regression ◽  
Demand Forecasting Model ◽  
Testing Algorithm

Proposed an agricultural machinery demand forecasting model based on the generalized regression neural network. This model is based on GRNN, using the circulation testing algorithm combined with k-fold cross validation for parameters optimization and network training, and achieves satisfying forecasting precision in the case of small samples. By using the data of total power agricultural machinery and relevant factors from the year 1995 to 2010 in Guangxi province, we tested and verified the effectiveness of the model.

LOW-COST TOTAL POWER RADIOMETER USING CONVENTIONAL DTH ANTENNA

2020 ◽  
Vol 79 (16) ◽  
pp. 1479-1487
Author(s):  
Samarendra Nath Sur ◽  
A. K. Singh ◽  
P. Chettri ◽  
R. Bera
Keyword(s):  
Low Cost ◽  
Total Power
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