This study was conducted to investigate the recipe and process of instant pickle by multiple steps with dry daylily as a raw material, and an orthogonal test was adopted to obtain the optimal recipe and process. The p...This study was conducted to investigate the recipe and process of instant pickle by multiple steps with dry daylily as a raw material, and an orthogonal test was adopted to obtain the optimal recipe and process. The pickling process of the instant flavored daylily was conducted at an optimal crisp-keeping Ca Cl2 concentration at 0.050%, cooking time of 5 min, pickling time of 6 h and a salt concentration of 4%. The effects of various factors on product taste were in order of salt concentrationcooking timepickling timeCa Cl2 concentration.The obtained product has the characteristics of strong fragrance, crisp delicious taste and unique flavor with stomachic effect.展开更多
Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithm...Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithms for average reward problems, a novel incremental algorithm, called R( λ ) learning, was proposed. Results and Conclusion The proposed algorithm is a natural extension of the Q( λ) learning, the multi step discounted reward reinforcement learning algorithm, to the average reward cases. Simulation results show that the R( λ ) learning with intermediate λ values makes significant performance improvement over the simple R learning.展开更多
基金Supported by the Fund for Independent Innovation of Agricultural Sciences in Jiangsu Province(CX(12)3080)~~
文摘This study was conducted to investigate the recipe and process of instant pickle by multiple steps with dry daylily as a raw material, and an orthogonal test was adopted to obtain the optimal recipe and process. The pickling process of the instant flavored daylily was conducted at an optimal crisp-keeping Ca Cl2 concentration at 0.050%, cooking time of 5 min, pickling time of 6 h and a salt concentration of 4%. The effects of various factors on product taste were in order of salt concentrationcooking timepickling timeCa Cl2 concentration.The obtained product has the characteristics of strong fragrance, crisp delicious taste and unique flavor with stomachic effect.
文摘Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithms for average reward problems, a novel incremental algorithm, called R( λ ) learning, was proposed. Results and Conclusion The proposed algorithm is a natural extension of the Q( λ) learning, the multi step discounted reward reinforcement learning algorithm, to the average reward cases. Simulation results show that the R( λ ) learning with intermediate λ values makes significant performance improvement over the simple R learning.
