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The instability disaster prediction model of tailings dam had been established, based on system analysis of the factors that caused the instability disaster of tailings dam, by selecting 6 prediction index, medium unit weight, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio and combining with using theory of the rough set and neural network. First the rough set...
Grey model and support vector machine are fit for prediction in the small size of data, their advantages and disadvantages are probed in this paper at first. And then, the combined model is proposed, which combines grey model and support vector machine with optimal weights. The weights are obtained and optimized by minimizing the sum of squared residuals standard. Some experiments compared with grey...
Iterative feedback tuning (IFT) method is a model-free technique for controller parameters tuning and control performance optimization with no system identification. In this paper, combined with Smith predictor, a model-free closed-loop parameters optimization method for big time delay system is presented, called IFT-Smith algorithm. An improved efficient performance criterion is proposed, which includes...
A synthesis approach of constrained robust model predictive control (SCRMPC) for systems with polytopic description is proposed. This proposal uses time-varying sequences of models in a polytope to forecast the model uncertainties and optimizes the terminal constrained set, the local controller and the terminal cost on-line. Using standard techniques, the problem is reduced to a convex optimization...
A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty outputs constrained by tolerable performance requirements. As in standard model predictive control, the...
Neural Networks are largely used in a vast number of applications, including time series prediction, function approximation, pattern classification. Recently Nonlinear Auto Regressive with eXogenous input (NARX) Recurrent Neural Networks has been used in to predict noisy and large time series (also referred as chaotic time series). This paper present a multiobjective optimized implementation of NARX...
Model predictive control strategy is based on the iterative resolution of a constrained optimization problem. In this context, the feasibility represents a central issue. The present paper concentrates on the use of the invariant sets to guarantee the feasibility of the predictive control law for reference tracking applications. In a first stage, two basic algorithms to approximate the maximal invariant...
For better predicting and optimizing the blasting parameters in underground deep-hole mining, 16 groups of deep-hole blasting parameters are collected and collated, combining rough set and artificial neuron network theory, an optimized model for basting parameters in underground mines' long-hole caving based on rough set and artificial neural network is set up. Adopting the rough set software for...
In the traditional feature selection, only a simple feature selection can be made, which will lead to the loss of information. In this paper, the requirement of weapon system economic analysis on the cost forecasting and the importance analysis of tactical and technical indicators were taken into account, moreover, considering the shortcomings of the traditional method of feature selection. A weighted...
In a decentralized supply chain system, it is very important to forecast the changes in the market in order to maintain an inventory level that is just enough to satisfy customer demand. A optimization-based control approach for supply chain networks is presented. The control strategy applies model predictive control principles to the entire supply chain networks, and supply chains whose dynamic behavior...
The social security level is an important factor during the process of the reformation of social security system. Whether it is fit for the level of the economic development has a crucial impact on the development of the social economy and the stabilization and solidification of our country. Higher or lower social security level influences the development of economics. Based on the Grey system and...
Considering the shortcomings of conventional cost prediction methods, neural network was adopted to establish the cost prediction model of equipment system, which could efficiently solve the problems on the determination of network structure. And due to the importance of parameters optimization in Neural Network model, rough set was used to optimize the model parameters. The experiment results show...
In 2005, after the RMB exchange rate reform, the RMB-USD exchange rate has been caused for concern. This article is based on the use of GARCH models to establish the prediction model of RMB-USD exchange rate and a new stimulated evolutionary optimization algorithm - ant colony algorithm applied to the model, hoping to provide a RMB-USD exchange rate for the model to predict accurately. We analyze...
The objective of this work is the development of DMC for a shell and tube heat exchanger and to address the difficulties in tuning a DMC. Although the PID controller is widely used for these types of applications there is still a need for optimization of conservation of energy. In this paper a model based predictive algorithm is used for controlling a temperature of a fluid stream using the shell...
Structural parameters and material behaviors of an in-service structure are changed due to various external factors influence. Its structural strength is hard to be clearly expressed by mathematical formula. Based on the Grey system and the new information principle, taking the mean relative error as objective function and taking the modified nth component of each variable as initial value of response...
After disaster, effective distribution of relief commodities to the affected areas is vital to minimize the loss. Generally speaking, the exact demand data are hard to obtain immediately after the disaster, which will cause difficulties to the decision-making process. In this paper, we present a prediction method of the relief demands after an earthquake. We also propose a distribution model considering...
To study raw series of non-homogeneous grey exponent law, this paper models the raw series without accumulating generator operation, optimizes derivative, and fits the raw series by non-homogeneous exponential function. At the same time, the paper has widened the range of application from homogeneous exponential function to non-homogeneous exponential function effectively and avoids the problem that...
The paper considers the application of Robust Optimization (RO) to Model Predictive Control (MPC). This optimization methodology incorporates the uncertain data, which means the data of an optimization problem is not known exactly at the time when its solution has to be determined. The robust optimization has been expanded and applied to various kinds of application, in this paper, it is shown the...
In this paper, we define two new unbiased GM (1, 1) models for the selection of initial conditions: starting-point fixed unbiased GM (1,1) (SUGM (1,1))and ending-point fixed unbiased GM (1, 1) (EUGM (1,1)), which are based on the unbiased GM (1, 1) regarding the 1-th and the n-th vector as the initial condition. Then it gave initial condition an amendment item respectively and carried out an optimization...
According to Principle of New Information Priority in grey theory, giving the new information larger weight in modeling can improve the effectiveness of gray model. For the GM (1, 1)model has very small samples, and the high overall simulation accuracy does not necessarily guarantee high prediction accuracy, we put forward weighted least square method to estimate parameters in GM (1, 1) model. Focusing...
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