fuzzy

this m lab file describes wcdma

MATLAB LSB İ MPORTANT

实现Q学习算法，动作选择策略为贪婪策略，存在收敛判断条件。(Implementing Q-learning algorithm, action selection strategy is greedy strategy.There is a convergence judgment condition.)

Writing C + + code, is to allow the use ns2 select the channel, which is our experimental multichannel implementation, hoping to share.

时间序列数据分析中的梅林变换工具，阐述了负荷预测的应用研究,matlab小波分析程序。( Time series data analysis Mellin transform tool, It describes the application of load forecasting, matlab wavelet analysis program.)

Introduction Matlab is an ideal tool for simulating digital communications systems, thanks to its easy scripting language and excellent data visualization capabilities. One of the most frequent simulation tasks in the field of digital communications is bit-error- rate testing of modems. The bit-error-rate performance of a receiver is a figure of merit that allows different designs to be compared in a fair manner. Performing bit-error-rate testing withMatlab is very simple, but does require some prerequisite knowledge.

上行虚拟MIMO算法，S-PF算法，D-PF算法，RR算法，Random算法，正交算法(Uplink Virtual MIMO algorithms, S-PF algorithm, D-PF algorithm, RR algorithm, Random algorithm, orthogonal algorithm)

复化梯形函数，复化辛普森函数，龙贝格函数matlab算法(Complex trapezoidal function, complex function Simpson, dragon berg function matlab algorithm)

Yaakov Bar-Shalom, X.-Rong Li,Thiagalingam Kirubarajan - Estimation with Applications to Tracking and Navigation - DynaEst toolbox (Yaakov Bar-Shalom, X.-Rong Li,Thiagalingam Kirubarajan- Estimation with Applications to Tracking and Navigation- DynaEst toolbox )

The problem of image registration subsumes a number of problems and techniques in multiframe image analysis, including the computation of optic flow (general pixel-based motion), stereo correspondence, structure from motion, and feature tracking. We present a new registration algorithm based on spline representations of the displacement field which can be specialized to solve all of the above mentioned problems. In particular, we show how to compute local flow, global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of the above problems. Using a spline-based description of the flow removes the need for overlapping correlation windows, and produces an explicit measure of the correlation between adjacent flow estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D projective scene geometry. We also provide results on a number of standard motion sequences.