pandey

小波阈值去噪，比较了软阈值，硬阈值及当今各种阈值计算方法和阈值函数处理方法的性能，通过信噪比及均方差的比较，得出各种的算法的优劣。(Wavelet threshold denoising compared soft threshold and hard threshold and the current method of calculating the various thresholds and thresholds approach the performance of function, through the signal to noise ratio and mean square deviation of the comparison, the various algorithms advantages and disadvantages.)

说明：  自编二值算术编码算法的matlab演示。从text.txt读取信息，编码过程及结果由matlab图形演示(binary arithmetic coding algorithm matlab demo. Reading information from Text.txt and the encoding process and the results are showed by the matlab graphics demo step by step.)

mimo msi canal incertain model

用粒子群算法进行无功优化，比较实用，希望对大家有帮助(Particle swarm algorithm with reactive power optimization, more practical, we want to help, 14 nodes)

matlab图形操作指令,可以方便很好掌握图形操作。(matlab graphics operations command)

通过ｂｉｄ　，ａｓｋ的分布，用以计算当前市场的交易活跃程度。可以分别统计出卖方触发单和买方触发单(Through the distribution of the bid, ask, for the current market trading activity. Respectively statistics triggered single and buyer seller trigger single)

宽带认知无线电频谱感知方法，两种常用方法，新颖方法(Broadband cognitive radio spectrum sensing method, two common methods, novel methods)

关于短路计算的一些源程序,同时是有关matlab的源程序(Short-circuit calculations on some of the matlab source code is also optimized source)

这是基于傅里叶变化的压缩感知稀疏分解方法，使用的是匹配追踪算法，对初学者很有帮助(This is based on Fourier transform of compressed sensing sparse decomposition method, using the matching pursuit algorithm, useful for beginners)

小波神经网络的源程序: 1.构造的非线性函数: 位于nninit_test.m 2.直接用WNN逼近非线性:Wnn_test.m, (内部调用小波函数) 3.遗传算法优化后逼近 :GA_Wnn_test.m (内部调用遗传算法的,初始化,适应度,解码函数)-genetic algorithm optimization WNN source : 1. Construction of the nonlinear function : nninit_test.m at 2. WNN directly with nonlinear approximation : Wnn_test.m. (internal called wavelet function) 3. Genetic Algorithm optimization approach : GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions) (Wavelet neural network source code: 1. Construction of the nonlinear function: at nninit_test.m 2. Wnn the direct use of nonlinear approximation: Wnn_test.m, (internal call wavelet function) 3. Genetic algorithm optimized approximation: GA_Wnn_test. m (internal call genetic algorithms, initialization, fitness, decoding function)-genetic algorithm optimization WNN source: 1. Construction of the nonlinear function: nninit_test.m at 2. WNN directly with nonlinear approximation: Wnn_test.m. ( internal called wavelet function) 3. Genetic Algorithm optimization approach: GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions))