Research on WNN Greenhouse Temperature Prediction Method Based on GA

Temperature in agricultural production has a direct impact on the growth of crops.The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature,but the temperature modeling of greenhouses is still the main direction at present.Neural network modeling relies on sufficient actual data to model greenhouses,but there is a widening gap in the application of different neural networks.This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm(GA-WNN).With the simple network structure and the nonlinear adaptability of the wavelet basis function,wavelet neural network(WNN)improved model training speed and accuracy of prediction results compared with back propagation neural networks(BPNN),which was conducive to the prediction and control of short-term greenhouse temperature fluctuations.At the same time,the genetic algorithm(GA)was introduced to globally optimize the initial weights of the original model,which improved the insensitivity of the model to the initial weights and thresholds,and improved the training speed and stability of the model.Finally,simulation results for the greenhouse showed that the model training speed,prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.

Design of greenhouse temperature and humidity wireless control system based on CC3200

Referring to the shortages that the process of traditional greenhouse measurement by using thermometer and hygrometer is complex,the measurement result is not accurate,and the control system operation is cumbersome,a greenhouse temperature and humidity(TH)control system based on CC3200 is designed.The system uses FPGA as the main controller,sends the TH signals to the wireless module CC3200 by controlling DHT22.The proposed system realizes the remote transmission of data and the automatic control of system.

Robust model predictive control for greenhouse temperature based on particle swarm optimization

Application of model predictive control(MPC)in horticultural practice requires detailed models.However,even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded uncertainties.To enforce robustness during the controller design stage,this paper proposes a particle swarm optimization(PSO)-based robust MPC strategy for greenhouse temperature systems.The strategy is based on a nonlinear physical temperature affine model.The robust MPC technique requires online solution of a minimax optimal control problem,which optimizes the tradeoff between set point tracking and cost requirements reduction.The minimax optimization problem is reformulated to a nonlinear programming problem with constraints.PSO is used to solve the reformulated problem and priority ranking of constraint fitness is proposed to guarantee that the constraints are satisfied.The results of simulations performed using the proposed control system show that the controller can effectively achieve the set point in the presence of disturbances and that it offers more suitable control variables,higher control precision,and stronger robustness than the conventional MPC.

Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.