By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power pla...By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.展开更多
Artificial neural network has unique advantages for massively parallel processing, distributed storage capacity and self-learning ability. The paper mainly constructs neural network identifier and neural network contr...Artificial neural network has unique advantages for massively parallel processing, distributed storage capacity and self-learning ability. The paper mainly constructs neural network identifier and neural network controller for system identification and control on temperature and hmnidity of heating and drying system of materials. And the paper introduces the structure and principles of neural network, and focuses on analyzing learning algorithm, training algorithm and limitation of the most widely applied multi-layer feed-forward neural network （ BP network） , based on which the paper proposes introducing momentum to improve BP network.展开更多
ln this research, the whole contact-type large-scale sow house with fer-mentation bed was designed. The planning area of the entire piggery was 5 700 m2 with workplace and green belts. The sow house was 93 m long and ...ln this research, the whole contact-type large-scale sow house with fer-mentation bed was designed. The planning area of the entire piggery was 5 700 m2 with workplace and green belts. The sow house was 93 m long and 33 m wide, a total of 3 069 m2, including office area of 60 m2 and aisle area of 107 m2. The fer-mentation bed had an area of 2 902 m2 with length of 88.7 m and width of 27.7 m. lts area accounted for 95% of the total area of sow house. The fermentation mattress had a depth of 80 cm, and had a volume of 2 321 m3, equivalent to 733 t of coconut chaff and rice chaff. On a large fermentation bed, the areas for boars, replacement gilts, pregnant sows, obstetric tables, nursery pigs, etc. were designed. The large-scale sow house with fermentation bed was equipped with the automatic feeding system, automatic sprinkler system, automatic positioning column for preg-nant sows, sows＇ obstetric table system, fanning wet curtain cooling system, video monitoring system, environmental monitoring （light, temperature, water, humidity, CO2, NH3） and automatic control system. Every farming area was equipped with feeding trough and water trough. The water though was fixed with overflow pipe for removing the extra water. The house could hold 500-head sows. Each sow occu-pied 4.9 m2 of the fermentation bed in average. The designed sow house had a maximum annual output of 10 000 piglets.展开更多
基金supported by the project of "SDUST Qunxing Program"(No.qx0902075)
文摘By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.
文摘Artificial neural network has unique advantages for massively parallel processing, distributed storage capacity and self-learning ability. The paper mainly constructs neural network identifier and neural network controller for system identification and control on temperature and hmnidity of heating and drying system of materials. And the paper introduces the structure and principles of neural network, and focuses on analyzing learning algorithm, training algorithm and limitation of the most widely applied multi-layer feed-forward neural network （ BP network） , based on which the paper proposes introducing momentum to improve BP network.
基金Supported by Chinese Ministry of Science and Technology(2012DFA31120)Natural Science Foundation of China(NSFC)(31370059)+2 种基金948 Project of Chinese Ministry of Agriculture(2011-G25)973 Program Earlier Research Project(2011CB111607)Project of Agriculture Science and Technology Achievement Transformation(2010GB2C400220)
文摘ln this research, the whole contact-type large-scale sow house with fer-mentation bed was designed. The planning area of the entire piggery was 5 700 m2 with workplace and green belts. The sow house was 93 m long and 33 m wide, a total of 3 069 m2, including office area of 60 m2 and aisle area of 107 m2. The fer-mentation bed had an area of 2 902 m2 with length of 88.7 m and width of 27.7 m. lts area accounted for 95% of the total area of sow house. The fermentation mattress had a depth of 80 cm, and had a volume of 2 321 m3, equivalent to 733 t of coconut chaff and rice chaff. On a large fermentation bed, the areas for boars, replacement gilts, pregnant sows, obstetric tables, nursery pigs, etc. were designed. The large-scale sow house with fermentation bed was equipped with the automatic feeding system, automatic sprinkler system, automatic positioning column for preg-nant sows, sows＇ obstetric table system, fanning wet curtain cooling system, video monitoring system, environmental monitoring （light, temperature, water, humidity, CO2, NH3） and automatic control system. Every farming area was equipped with feeding trough and water trough. The water though was fixed with overflow pipe for removing the extra water. The house could hold 500-head sows. Each sow occu-pied 4.9 m2 of the fermentation bed in average. The designed sow house had a maximum annual output of 10 000 piglets.
