Due to the non-linearity behavior of the precision positioning system, an accurate mathematical control model is difficult to set up, a novel control method for ultra-precision alignment is presented. This method reli...Due to the non-linearity behavior of the precision positioning system, an accurate mathematical control model is difficult to set up, a novel control method for ultra-precision alignment is presented. This method relies on neural network and alignment marks that are in the form of 100μm pitch gratings. The 0-th order Moire signals＇ intensity and its intensity rate are chosen as input variables of the neural network. The characteristics of the neural network make it possible to perform self-training and self-adjusting so as to achieve automatic precision alignment. A neural network model for precision positioning is set up. The model is composed of three neural layers, i.e. input layer, hidden layer and output layer. Driving signal is obtained by mapping Moire signals＇ intensity and its intensity rate. The experimental results show that neural network control for precision positioning can effectively improve positioning speed with high accuracy. It has the advantages of fast, stable response and good robustness. The device based on neural network can achieve the positioning accuracy of ± 0. 5μm.展开更多
An intelligent shearer height adjusting system is a key technology for mining at a man-less working face. A control strategy for a shearer height adjusting system based on a mathematical model of the height adjusting ...An intelligent shearer height adjusting system is a key technology for mining at a man-less working face. A control strategy for a shearer height adjusting system based on a mathematical model of the height adjusting mechanism is proposed. It considers the non-linearity and time variations in the control process and uses Dynamic Fuzzy Neural Networks （D-FNN）. The inverse characteristics of the system are studied. An adaptive on-line learning and error compensation mechanism guarantees sys- tem real-time performance and reliability. Parameters from a German Eickhoff SL500 shearer were used with Maflab/Simulink to simulate a height adjusting control system. Simulation shows that the trace error of a D-FNN controller is smaller than that of a PID controller. Also, the D-FNN control scheme has good generalization and tracking performance, which allow it to satisfy the needs of a shearer height adjusting system.展开更多
Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of ...Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.展开更多
A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algor...A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simulation results indicate that fuzzy recurrent neural network controller has perfect dynamic and static performances .展开更多
基金The Natural Science Foundation of Higher EducationInstitutions of Jiangsu Province (No.04KJB510073).
文摘Due to the non-linearity behavior of the precision positioning system, an accurate mathematical control model is difficult to set up, a novel control method for ultra-precision alignment is presented. This method relies on neural network and alignment marks that are in the form of 100μm pitch gratings. The 0-th order Moire signals＇ intensity and its intensity rate are chosen as input variables of the neural network. The characteristics of the neural network make it possible to perform self-training and self-adjusting so as to achieve automatic precision alignment. A neural network model for precision positioning is set up. The model is composed of three neural layers, i.e. input layer, hidden layer and output layer. Driving signal is obtained by mapping Moire signals＇ intensity and its intensity rate. The experimental results show that neural network control for precision positioning can effectively improve positioning speed with high accuracy. It has the advantages of fast, stable response and good robustness. The device based on neural network can achieve the positioning accuracy of ± 0. 5μm.
基金support for this work, provided by the National High Technology Research and Development Program of China (No2008AA062202)China University of Mining & Technology Scaling Program
文摘An intelligent shearer height adjusting system is a key technology for mining at a man-less working face. A control strategy for a shearer height adjusting system based on a mathematical model of the height adjusting mechanism is proposed. It considers the non-linearity and time variations in the control process and uses Dynamic Fuzzy Neural Networks （D-FNN）. The inverse characteristics of the system are studied. An adaptive on-line learning and error compensation mechanism guarantees sys- tem real-time performance and reliability. Parameters from a German Eickhoff SL500 shearer were used with Maflab/Simulink to simulate a height adjusting control system. Simulation shows that the trace error of a D-FNN controller is smaller than that of a PID controller. Also, the D-FNN control scheme has good generalization and tracking performance, which allow it to satisfy the needs of a shearer height adjusting system.
基金Funded by the Natural Science Foundation of Chongqing City(No.2005BB7250)
文摘Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.
文摘A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simulation results indicate that fuzzy recurrent neural network controller has perfect dynamic and static performances .
