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In crystallization process there are strong coupling between the temperature control and the level control in the tank, and the parameter is time-varying, so it is difficult to apply general PID control. The self-adjusting PID control method based on diagonal recurrent neural network (DRNN) is introduced in this paper. According to the influence of object's parameter to system output performance,...
Generalized PID neural network (GPIDNN) has recently received more attention in industry application. To investigate the control of long time-delay systems with GPIDNN control system, both the structure and the algorithm were presented in this paper, the real-time simulation to a main steam temperature control system was also carried out. The results show that GPIDNN is less sensitive to variation...
This paper describes a three-phase full control rectification circuit controller which is widely used in the system of speed regulation of DC motor. Conventional PID controller with double close-loops has been used in speed control of separately excited DC motor at present. But under conditions of actual operation we find that it isn't suitable for the high performance cases, because of the low robustness...
Multi-agent supply chain management has recognized as an effective methodology for supply chain management. This paper addresses the state of the art of multi-agent supply chain management, particularly the three aspects of multi-agent supply chain management, i.e., modeling and simulation, negotiation, and coordination, where the methodologies is addressed and critical comments are put forward, respectively
In process of SCM, the information is distributed and heterogeneous in nature. And the decisions are often optimized locally but do not assure a global optimization for the whole supply chain. To address these problems, an agent-based architecture has been developed which viewed SCM as consisted of flows and agents two objects. And the agents were classified into structural and functional agents to...
This paper presents the design, development of dynamic load simulator based on dynamic fuzzy neural networks (D-FNNs) controller. Dynamic load simulator (DLS) can reproduce desired load torque acting on loaded object to test its performance and stability. In DLS, the redundancy torque caused by the motion of loaded object has a very poor effect on the loading accuracy. So a simplified dynamic model...
Using a batch learning scheme and a hybrid learning rule, i.e. BP algorithm is applied to the learning of premise parameters, while least square algorithm to the learning of consequent parameters, an ANFIS system for ship autopilot with two inputs and one output, three fuzzy zones, nine fuzzy rules is trained. Training data come from a PD course control system, then the trained ANFIS autopilot controls...
The overall performance of a complex product generally depends on a number of specifications distributed in multi-teams from different disciplines. Multidisciplinary simulation analysis has been used widely in multidisciplinary design process. However, the knowledge discovery keeps bottleneck yet in building knowledge base for multidisciplinary design. In this paper, firstly, a framework of knowledge...
An adaptive fuzzy control strategy for hydro-turbine governor is presented in this paper. Considering the complex dynamic characteristic and uncertainty of the hydro-turbine governor model and taking the static and dynamic performance of the governing system as the ultimate goal, the novel controller combined the classical PID control theory with adaptive fuzzy control theory is designed. The presented...
In this paper, based on the relationships among customer satisfaction, repeated purchases, customer loyalty, complaint-voicing rate and complaint-dealing ability, a model is built to analysis customer flow resulting from the complaint management with the system dynamics method. Applying Vensim 5.0, customer flow is simulated with the data from China's mobile market. Through analyzing the simulation...
Parameter identification for mechanical servo systems with nonlinear friction term is very difficult, and linear identification techniques are not adoptable because that the parameters can not be linear parameterized as well as the local minimum problem. Based on genetic algorithms, this paper presented a two-step offline method for the parameter identification of mechanical servo embedded with LuGre...
Radial basis function (RBF) neural network (NN) is powerful computational tools, which have been used extensively in the areas of pattern recognition, systems modeling and identification due to the advantages of simple construction, adaptability and robustness. This paper presents a novel approach of single neuron PID model reference adaptive control (MRAC) control based on RBF neural network on-line...
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