Discrimination of a Thermodynamic Object Based on a Minimum Resource Allocation Network
Jianhong Lü · Journal of Engineering for Thermal Energy and Power · 2007
The establishment of a comprehensive nonlinear model for a thermodynamic process serves as a basis for the overall optimization of a thermodynamic control system.However,it is difficult for a static neural network to establish a model for nonlinear dynamic processes.A resource allocation network(RAN) lends itself to dynamically adjust the network parameters while an extension Kalman filter(EKF) algorithm can accelerate the converging speed.By organically combining the above-mentioned methods and adding on this basis pruning tactics and a slidingwindow root-mean-square criterion,an improved minimum resource allocation network(MRAN) can be formed.The improved MRAN has been applied to the nonlinear dynamic modeling of a typical thermodynamic process.The simulation results show that the MRAN features a compact network structure and high modeling accuracy,thus making it suitable for on-line applications.Finally,analyzed is the impact of network initial parameters on its performance.