Research On Thermal Identification Using Quantum Compution And Its Application

There are always some sytems that people cannot describe them by using existing knowledge during the process of recognizing and transforming the objective world. System identification is the link between reality and mathematical model, which can be defined as the way of systematic modeling according to measured serious systematic signals. In the term of optimization calculation, quantum computing has incomparable advantages over classic optimization calculation in some aspects.This article identifies thermal process by using the way of quantum computing and quantum optimization, and studies linear and single-output system transfer function model identification, multivariable subspace identification and nonlinear neural network identification and so on. The main innovative efforts are:(1) In order to improve convergence speed and precision of optimization in quantum particle swarm optimization (QPSO), an improved QPSO algorithm was presented. First, chaotic sequences are used to initialize the origin angle position of particle; Second, mutation algorithm is introduced, which can effectively increase diversity of population, and also can avoid premature convergence. The test results of function optimization show that the proposed algorithm has better optimized effect. The improved algorithm proposed in this paper was applied to identify the classic adaptive IIR model, and results proved the validity of the algorithm. On the basis of DCS, a general-purpose identification algorithm modular for thermal object model is programmed, and it is applied to the identification of circulating fluidized bed power plant, achieving satisfactory results.(2)We use examples to prove that the state subspace identification method is a kind of discriminating identification method. In order to obtain consistent unbiased estimation parameters, two sections identification method is puts forward on the basis of classical state subspace identification and optimization algorithm. First, the initial values of the object are identified by using classical state subspace identification, then we use improved quantum partial swarm algorithm to get the consistent unbiased estimation parameters. Examples show the effectiveness of the presented algorithm. At last, a coordinated control system in power plant is identified, and results showed that the presented method can be used in identification MIMO system in industrial process, and it can get good results.(3) Quantum genetic algorithm is a probability optimization method which is based on quantum compute principle. The precision and the rate of convergence are impacted by rotation angle. Aiming at the shortcoming of fuzzy quantum genetic algorithm (FQGA), quantum-inspired crossover method was introduced to FQGA, and a novel quantum genetic algorithm was put forward. Using this method, an identification algorithm of nonlinear systems is presented. This method is characterized by estimating parameters such as weight, width and central position of RBF NN using the new quantum genetic algorithm. High velocity and accuracy of the method enable nonlinear systems to be efficiently identified by using RBF NN. The results of identifying typical nonlinear function demonstrate that the precision and the rate of convergence are improved. A special program was compiled to identify the object model of the thermal process, and the dynamic process between primary air feed rate and bed temperature was identified. The results show that accuracy of the approach is high and has a certain practical value.