fft

本代码使用高斯绝对稀疏信号进行重构，采用的重构算法是SAMP，重构效果好！(This code uses the absolute sparse Gaussian signal reconstruction, reconstruction algorithm uses a SAMP, good remodeling effect!)

getFnumber and round_fix one point

programs for economic load dispatch using hybrid genetic algorithm part 3

STFT短时傅里叶变换，针对一个和两个调频信号进行分解，变换结果可以显示(STFT transform for a short time, and two FM signal decomposition, transform the results can show)

遗传粒子群、混沌粒子群、基本粒子群三者对比，对一个判断方程，输出其收敛曲线，本人毕业设计的核心精华！(Genetic particle swarm, chaotic particle swarm, and basic particle swarm, equations for a judgment, the output curve of its convergence, the core essence of graduation design.)

自适应滤波算法的MATLAB代码:run_lms_mvdr(Adaptive filtering algorithm MATLAB code: run_lms_mvdr)

8085 instruction set

Speech Recognition with Tdnn by simulink

粒子群搜索的matlab程序，对于一个隐函数寻优，四维隐含数嵌入在子函数中，可根据需要更改，(PSO Matlab search procedures, an implicit function optimization, 4D implied several embedded in the Functions, may need to change.)

支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。( Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation.)