This folder has some scritps that you may find usefull. All of it comes from questions that I ve received in my email. If you have a new request/question, feel free to send it to marceloperlin@gmail.com. But please, don t ask me to do your homework. Passing_your_param0 This folder contains instructions (and m files) for passing you own initial parameters to the fitting function. I also included a simple simulation script that will create random initial coefficients (under the proper Bounds) and fit the model to the data.
標簽： that questions scritps usefull
上傳時間： 2013-12-28
上傳用戶：netwolf
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower Bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
標簽： processing ballistic the tracking
上傳時間： 2014-10-31
上傳用戶：yyyyyyyyyy
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower Bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
標簽： processing ballistic the tracking
上傳時間： 2014-01-14
上傳用戶：奇奇奔奔
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower Bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
標簽： processing ballistic the tracking
上傳時間： 2013-12-22
上傳用戶：asddsd
SuperLU is a general purpose library for the direct solution of large, sparse, nonsymmetric systems of linear equations on high performance machines. The library is written in C and is callable from either C or Fortran. The library routines will perform an LU decomposition with partial pivoting and triangular system solves through forward and back substitution. The LU factorization routines can handle non-square matrices but the triangular solves are performed only for square matrices. The matrix columns may be preordered (before factorization) either through library or user supplied routines. This preordering for sparsity is completely separate from the factorization. Working precision iterative refinement subroutines are provided for improved backward stability. Routines are also provided to equilibrate the system, estimate the condition number, calculate the relative backward error, and estimate error Bounds for the refined solutions.
標簽： nonsymmetric solution SuperLU general
上傳時間： 2017-02-20
上傳用戶：lepoke
matlab環境下目標函數為求最大值，且解非負整數解 ％Bounds 邊界約束 ％Myfun 為目標函數 ％num 初始種群數 ％N 最大迭代次數 ％CP 交叉概率 ％P 突變概率 ％f 目標最優解 ％x 最優解向量
上傳時間： 2017-03-06
上傳用戶：Ants
Statistical-Learning-Theory The goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error Bounds. This tutorial introduces the techniques that are used to obtain such results.
標簽： statistical Statistical-Learning-Theory learning theory
上傳時間： 2017-07-15
上傳用戶：363186
Abstract—Stable direct and indirect decentralized adaptive radial basis neural network controllers are presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Due to the functional approximation capabilities of radial basis neural networks, the dynamics for each subsystem are not required to be linear in a set of unknown coeffi cients as is typically required in decentralized adaptive schemes. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown Bounds.
標簽： decentralized controllers Abstract adaptive
上傳時間： 2017-08-17
上傳用戶：gdgzhym
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao Bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
標簽： 傳感器網絡
上傳時間： 2016-11-27
上傳用戶：xxmluo
