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matlab写的人工神经网络的例子，附有处理图片和注释(matlab written examples of artificial neural networks with image processing and annotation)

NTFS完整可编译代码！由weolar修改制作！(NTFS complete code can be compiled! Produced by the modified weolar!)

说明：  模拟退火算法应用于光学模拟方向的程序代码(Simulated annealing algorithm is applied to the direction of the optical simulation code)

说明：  上传RBF源程序，希望能对大家学习有所帮助，并请提出宝贵的意见和建议！(upload source, in hopes of learning we can help, and requested the valuable advice and suggestions!)

Hooke And Jeeves Using Line Searches nonlinear optimization

contain step to detection human skin background using C++. From RGB as input convert to Ycrcb

spvwm 3 level inverter

说明：  任务：设计一个4×2.5Gbit/s 长距离光纤传输系统，并利用OptiSystem 仿真验证。 要求： 1．采用波分复用技术，波长设置以100GHz 为间隔，频率分别为193.1THz、193.2THz、193.3THz、193.4THz，每个波长传输速率为2.5Gbit/s（NRZ），传输距离为300km。采用常规光纤，光纤损耗系数为0.25dB/km。 2．单通道光发送机最大输出功率为0dBm，光接收机的接收光功率不低于－26dBm，过载点为－10dBm。 3．可以采用EDFA 作为线路放大器，其中EDFA 的输入功率限制在－18～2dBm，最大输出功率20dBm，增益范围为13～25dB，噪声指数4.0dB。(Task: Design a 4 x 2.5 Gbit/s long-distance optical fiber transmission system, and use OptiSystem simulation to verify. Requirement: 1. Using wavelength division multiplexing technology, the wavelength is set at 100GHz intervals, with frequencies of 193.1 THz, 193.2 THz, 193.3 THz and 193.4 THz, respectively. The transmission rate of each wavelength is 2.5 Gbit/s (NRZ), and the transmission distance is 300 km. The loss coefficient of conventional optical fibers is 0.25dB/km. 2. The maximum output power of a single-channel optical transmitter is 0 dBm, and the receiving power of an optical receiver is not less than -26 dBm, and the overload point is -10 dBm. 3. EDFA can be used as line amplifier, where the input power of EDFA is limited to - 18-2dBm, the maximum output power is 20dBm, the gain range is 13-25dB, and the noise index is 4.0dB.)

《MATLAB R2008图形与动画编程实例教程》的源代码--第六章(" MATLAB R2008 examples of graphics and animation programming tutorial" source code- Chapter VI)

基于统计的模式识别工具包The core of the STPRtool is comprised of statistical PR algorithms described in the monograph Schlesinger, M.I., Hlav′aˇc, V: Ten Lectures on Statistical and Structural Pattern Recognition, Kluwer Academic Publishers, 2002(Statistical Pattern Recognition Toolbox for Matlab)