Abstract In order to overcome the bad precision of fitted error, lower accuracy optimization results and other flaws, when extraction technology of the lotus leaf alkaloids was optimized by response surface method or regression analysis method, a linear constraint optimization method based on BP neural network is proposed. The testing program of three factors, three level was designed, which selected the hydrochloric acid mass fraction, ultrasound time, liquid-solid ratio as experimental factors. Taking the experiment data as training sample, the BP neural network model of the lotus leaf alkaloid yield and the influencing factors was obtained, and it was optimized by the proposed optimization method. The optimal parameter combination of extraction technology for lotus leaf alkaloid was obtained as follows: extraction temperature 60 °C, ultrasonic power 500W, hydrochloric acid mass fraction 0.3%, ultrasonic time 43 min, liquid-solid ratio 27, the yield of lotus leaf alkaloids under this process condition is 4.26 mg/g. It better than the best extraction technology obtained by response surface method. The obtained results is used for verification experiment, the verification results shown that the method has high fitting accuracy and stable optimization results, which optimize the extraction technology for lotus leaf alkaloid.

ABSTRACT To solve the poor fitting degree of errors in multiobjective parameter optimization and low accuracy, a multiobjective optimization method based on a BP neural network was proposed. By taking the 1ZT-210 type whole-straw returning device as the research object, a BP neural network model on power consumption, straw returning rate and the influencing factors was obtained. By optimizing the model by the proposed method, the optimal parameter combination of the test factors was as follows: the advancing speed of the device was 0.65 km/h, the blade roll rotating speed was 210 rpm, the blade installation angle was 55o, the minimum power consumption was 9.82 kW and the maximum straw returning rate was 93.23%. Under such test conditions, the minimum power consumption was 10.75 kW, and the straw returning rate was 92.46%, which were all better than those obtained by the regression analysis method. Finally, a verification test was conducted on the results of BP neural network optimization. The power consumption of the test was 10.04 kW, the absolute error was 0.22 kW and the relative error was 2.24%. For a straw returning rate of 93.11%, the absolute error was -0.12% and the relative error was 0.13%. The test results indicated that the optimization method was feasible.

ABSTRACT The parameter optimization of the tractor's steering trapezoid mechanism is a traditional optimization problem, and the teaching-learning-based optimization (TLBO) has a better solving ability for parameter optimization of the tractor's steering trapezoid. However, the teacher stage and student stage of TLBO limit the accuracy and stability and the ability to jump out of the local optimization solution. To obtain an optimal solution with an higher accuracy, an improved adaptive direction strategy teaching-learning-based optimization (IADS-TLBO) was used. This improved the feedback stage based on the adaptive direction strategy teaching-learning-based optimization (ADS-TLBO). The IADS-TLBO was verified by three different testing functions, and the results showed that the improved ideas are valid and feasible. Finally, the IADS-TLBO was used to optimize the steering trapezoid mechanism of JOHN DEERE T600. The optimal parameters obtained were as follows: the bottom angle was 35.4º, and the steering arm length was 154 mm. A verification experiment was conducted in the farm tool laboratory of Northeast Agricultural University (China). The experimental results showed that the average bottom angle was 35.48º, and the relative error between the measured and optimized bottom angles was 0.23%, which is less than 5%. This result showed that the results obtained by IADS-TLBO were reliable.