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Application of the recurrent multilayer perceptron in modelingcomplex process dynamics 总被引:1,自引:0,他引:1
A nonlinear dynamic model is developed for a process system, namely a heat exchanger, using the recurrent multilayer perceptron network as the underlying model structure. The perceptron is a dynamic neural network, which appears effective in the input-output modeling of complex process systems. Dynamic gradient descent learning is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over a static learning algorithm used to train the same network. In developing the empirical process model the effects of actuator, process, and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response of various testing sets. Extensive model validation studies with signals that are encountered in the operation of the process system modeled, that is steps and ramps, indicate that the empirical model can substantially generalize operational transients, including accurate prediction of instabilities not in the training set. However, the accuracy of the model beyond these operational transients has not been investigated. Furthermore, online learning is necessary during some transients and for tracking slowly varying process dynamics. Neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. 相似文献
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针对中小型企业销售管理的现状,利用J2EE架构技术设计了面向中小型企业的销售管理信息系统,该系统具有良好的跨平台性和可移植性,并且经过测试运行,达到了预期的效果。 相似文献
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通过对小湾电站左岸低缆项目实行项目风险管理的方法及成果介绍,指出了在工程领域对项目实行风险管理是一种重要的管理手段和方法,值得推广和研究. 相似文献
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Mobile robots can be used in many applications, such as exploration, search and rescue, reconnaissance, security, and cleaning.
Mobile robots usually carry batteries as their energy source and their operational time is restricted by the finite energy
available from the batteries. Therefore, energy constraints are critical to the service time of mobile robots. This paper
investigates the minimum-energy control problem for translational trajectory generation, which minimizes the energy drawn
from the batteries. Optimal control theory is used to find the optimal velocity trajectory in analytic form. To demonstrate
energy efficiency obtainable, we performed simulations of minimum-energy velocity control and compared the results with loss-minimization
control and energy-optimal trapezoidal velocity profiles. Simulation results showed that significant energy savings can be
achieved, of up to 9% compared with loss-minimization control and up to 10% compared with energy-optimal trapezoidal velocity
profile. We also performed an actual robot experiment using Pioneer 3-AT platform to show the validity of the proposed minimum-energy
velocity control. The experimental results revealed that the proposed minimum-energy velocity control can save the battery
energy up to 10% compared with loss-minimization control.
Categories (3): Robot control, (5): RobotMotion Planning 相似文献