Papers by Keyword: Parameter Estimation

Abstract: It is difficult to estimate the parameters of Weibull distribution model using maximum likelihood estimation based on particle swarm optimization (PSO) theory for which is easy to fall into premature and needs more variables, ant colony algorithm theory was introduced into maximum likelihood method, and a parameter estimation method based on ant colony algorithm theory was proposed, an example was simulated to verify the feasibility and effectiveness of this method by comparing with ant colony algorithm and PSO.This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text.

Abstract: Concerning the salient features of components energy consumption model in air conditioning system, this paper proposes a half-online identification algorithm to estimate the parameters of the models. The algorithm monitors the error online. When the error reaches the upper limit, one-time least square estimation is used to identify the parameter and update the model by means of current data within a certain time window. Experimental studies are conducted on a fan energy consumption model, which parameters are varying in real time. The estimation results show that the algorithm can greatly reduce the computational burden and possession of resources, while ensure good estimation performance.

Abstract: We adopt analog simulation method of Monte Carlo in this paper. Under the circumstance of different prior information, in this paper we also make researches on the value assessment accuracy of the samples’ total parameters of up-and-down and langlie methods, and figure out the application condition and range of these two sensitivity experimental methods.

Abstract: Time series analysis has been extensively used in many fields, such as system identification, modeling and data predication, and played an important role in system design, planning and performance analysis. The focus of time series application study is how to improve the accuracy and computation speed of the parameter estimation. Many researchers have carried out system modeling study by applying time series analysis and have gained their research results. The traditional methods such as maximum likelihood estimation, moment estimate and least square estimate which exit the defect of low precision, poor convergence and parameter estimation white noises coupling, are mostly utilized in parameter estimation for model. Taking this as basis the data forecasting and anomaly detection are conducted, which is hard to ensure the system’s stability. Different from the traditional algorithm, this paper proposes a new weighted iterative stage parameter estimation algorithm which avoids the coupling with white noise estimation of ARMA model parameter and improves the accuracy of parameter estimation. In theory, this algorithm tends to provide a good convergence performance. The experimental results based on ARIMA model show that the algorithm can improve the accuracy of parameter estimation and provide a good convergence performance.

Abstract: In this paper we solve the three-dimensional coordinate that the satellite is relative to the geocentric coordinate under certain conditions, making use of the satellite orbit standard trajectory differential equations. By means of the method of tri-parametric equation fitting, we confirm the three-dimensional trajectory function of target flying object to geocentric coordinate in double satellites observation conditions, and analyze theoretical errors.

Abstract: The frequency that extreme events appear in the life is low,but once it appears,the impact will be significant; many scholars have conducted in depth research and found that statistical theory of extreme value. The theory of extreme statistics plays a more and more important role in many fields such as automatic control, assembly line etc. This paper,makes an in-depth research towards the characteristics and parameter estimation of the extreme value statistical models,as well as the application,mainly analyzes the Bayes parameter estimation method of extreme value distribution,the extreme value distribution theory and Copula function random vector model.

Abstract: Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. However, there still remain many problems such as the randomness of detector generation, incompleteness of self-set and the generalization ability of detectors, which would cause a lot of loopholes in non-self-space. This paper proposes a heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self-space in the first distribution. The algorithm proposed can distribute real value detectors through omission data based on the methods of partition and movement. A method is then proposed to solve the problem on how to get the optimal solution to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that the algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.

Abstract: The main objective of time series analysis is to develop models that can establish the relation between variables,the paper ,An improved method of the RBF is proposed,which is a five-layered network structure comprising of an input layer,wavelet layer, product layer, output layer and polynomial regressive weight layer.which uses an online optimization approach, the method uses an offline learning method known as SNPOM,the polynomial weights are updated many times during the process of looking for the search direction to update the nonlinear parameters. The experiment showed that the optimization technique can speed up the convergence rate of nonlinear model during the learning process.

Abstract: according to the real motion blur image restoration problems, analyze the difference between the image features and Simulation of real blurred images, this paper proposes a method that applied to real image degradation parameter estimation. First calculate the degraded image using cepstrum, taking the cepstrum to binary image using absolute value of minimum gray as the threshold, and then remove the center cross bright line; and then use formula of point to line to calculate the distance of bright fringe direction of binary image, that is direction of motion blur; the direction of motion blur is rotated to the horizontal direction by the degraded image center of rotation axis, divided the autocorrelation method to calculate fuzzy scale. To estimate the point spread function is take into the Wiener filtering algorithm to recover images, image restoration effect prove that parameter estimation results are correct.

Abstract: This paper deals with the parameter identication problem for Hammerstein systems with two-segment preload nonlinearity. Taking into account the complexity of Hammerstein systems, we use theWeierstrass approximation theorem to convert a Hammerstein system into a special form that has linear-in-parameters, and propose a stochastic gradient algorithm to estimate all unknown parameters of Hammerstein systems. Furthermore, a modified stochastic gradient algorithm is given to improve the convergence rate. The applicability of the approach is illustrated by a simulation example.