the-whole-differential-cost

用MATLAB开发的BP算法源程序,对你或许有所帮助。(Developed using MATLAB source BP algorithm)

对MATLAB软件在控制领域的应用进行了详细介绍。内容包括使用DAS工具箱函数和ActiveX控件设计数据采集与控制程序；使用串口I/O流和MSComm控件设计串口通信程序等。每种程序设计方法都提供了多个典型应用实例，每个实例都提供了线路图、详细的程序设计步骤和完整的源代码可供自动化、计算机应用、电子信息、机电一体化、测控仪器等专业的大学生、研究生及相关技术人员学习和参考。 (MATLAB software in the control area of the application in detail. Content including the use of ActiveX controls DAS Design Toolbox functions and data acquisition and control procedures use the serial port I/O design flow and MSComm control serial communication procedures. Each programming methods provide a more typical application, each instance provides a road map, detailed program design steps and the complete source code for automation, computer applications, electronic information, mechanical and electrical integration, measurement and control instruments professional students, graduate students and relevant technical staff to learn and reference.)

matlab基础学习必备书籍，内容详细，适合新手(matlab-based learning essential books, detailed, suitable for novice)

MFSK simulation code for 24GHz radar, including simulation of MFSK waveform design and velocity and range measuring for moving targets.

Function matlab of sign-data algorithm

(ebook-pdf) - Engineering - Using MATLAB 6 (Graphics, 3D).pdf Overview of MATLAB Graphics – plot editing mode and the Property Editor. • Basic Plotting – plotting vector and matrix data in 2-D representations. • Formatting Graphs – describes how to customize the format of a plot to help explain your data. This section includes information about adding labels, titles, and annotations to a plot.

说明：  无线传感器网络LEACH算法的matlab模型(Wireless sensor networks LEACH algorithm matlab model)

基于蒙特卡罗方法的试卷难度分布研究,Monte Carlo method based on the difficulty of distribution of papers(Monte Carlo method based on the difficulty of distribution of papers)

matlab实现的目标跟踪算法，经调试能用。(Target tracking algorithm matlab implementation, debugging can be used.)

ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly so newdata = R*data + T . newdata are transformed data points to fit model see help icp for more information (ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model- matrix with model points, data- matrix with data points, OUTPUT: R- rotation matrix and T- translation vector accordingly so newdata = R*data+ T . newdata are transformed data points to fit model see help icp for more information )