TOOLBOX_calib

matlab非常重要的几点经验，非常值得留意和注意。(matlab a few very important experience, very worthy of attention and attention.)

模糊分类论文pdf文件以及Matlab code源码，对学习很有帮助。(A Simple Fuzzy Classifier based on Inconsistency Analysis of Labeled Data)

FIR(Finite Impulse Response)滤波器：有限长单位冲激响应滤波器，是数字信号处理系统中最基本的元件，它可以在保证任意幅频特性的同时具有严格的线性相频特性，同时其单位抽样响应是有限长的，因而滤波器是稳定的系统。因此，FIR滤波器在通信、图像处理、模式识别等领域都有着广泛的应用。 (FIR (Finite Impulse Response) filters: finite impulse response filter unit, digital signal processing system is the most basic components, it can ensure that any increase in the frequency characteristics of both a strictly linear phase frequency characteristics, while its unit sample response is finite, and therefore the system filter is stable. Therefore, FIR filters in communications, image processing, pattern recognition and other fields have a wide range of applications.)

说明：  用matlab写的水果分类系统源码，很值得学习，(Written by matlab source fruit classification system, it is worth learning,)

Matlab辅助激光光学分析与应用（2014年 第三版）(Matlab assistting laser optical analysis and application)

基于BP算法的massive mimo的均衡算法(Equalization algorithm for massive MIMO based on BP)

fisher例子程序必看fisher例子程序必看fisher例子程序必看(Examples of programs fisher must-see examples of programs fisher must-see examples of procedures for must-see fisher)

说明：  IEEE30算例,已经可以直接使用，用于基本的潮流算法测试(IEEE30 case it can be used to test power flower algorithm)

说明：  qpsk matlab

排课问题是一个有约束的、多目标的组合优化问题，并且已经被证明是一个NP完全问题。 遗传算法借鉴生物界自然选择和自然遗传机制，使用群体搜索技术，尤其是用于处理传统搜索方法难以解决的复杂的和非线性的问题。经过近40年的发展，遗传算法在理论研究和实际应用中取得了巨大的成功，本文将遗传算法用于排课问题的求解，首先讨论了排课问题中的影响因素、主要约束条件、求解目标和难点，并用数学模型完整地描述了排课问题。其次对多个模糊排课目标进行了定量分析，建立了排课优化目标空间。针对排课问题研究了染色体编码方式以及遗传算子的设计，提出了适应度函数的计算方法。最后对排课问题进行了实验。实验结果表明，其过程的目标值跟踪显示，算法稳健趋优，所得结果令人满意。(Course Scheduling problem is a constrained, multi-objective optimization problem, and has proven to be a NP complete problem. Genetic algorithms reference biosphere and the natural genetic mechanism of natural selection, using the group search technology, particularly the traditional search methods for handling complex and difficult to solve nonlinear problems. After nearly 40 years of development, the genetic algorithm in the theoretical study and practical application was a great success, this paper genetic algorithm for solving the course timetabling problem, first discussed the impact of factors in the course arrangement, the main constraints, to solve goals and difficulties, and a complete mathematical model to describe the course arrangement. Arranging multiple fuzzy goals followed by a quantitative analysis, the optimal target Arranging space. Arranging for the Study of the chromosome coding and genetic operators design, proposed fitness function is calculated. Finally, the co)