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KAVERAGE例程必看看KAVERAGE例程必看看(KAVERAGE routine will definitely take a look to see if KAVERAGE routine)

This code can be use in picking of seismic data.

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)

心电图的特性描述，可用matlab实现。(ECG characterization, available matlab achieve.)

数学建模：自动车床数学建模：自动车床数学建模：自动车床数学建模：自动车床数学建模：自动车床数学建模：自动车床(Mathematical modeling: automatic lathes)

独立分量分析中的时域预测代码 matlab格式 希望有用 (independent component analysis of time-domain code forecast hope Matlab format useful)

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这是GM（1,1）灰色预测的原理，为PDF格式。可进行对一些数据的预测工作。(This is the principle of GM (1,1) gray prediction is in PDF format. Forecasting can be carried out on some of the data.)

语音识别程序。有需要的抓紧啦。用MATLAB实现的。(Speech recognition procedures. You need to have the grasp. Achieved using MATLAB.)

MATLAB implementation of SPIHT (without Arithmatic coding stage). ％ Performance evaluation : this toolbox VS. SPIHT ％ Test image: lena512.raw ％ bpp SPIHT(dB) This code(dB) ％ 0.1000 29.8107 29.3202 ％ 0.2000 32.7202 32.2514 ％ 0.3000 34.5479 34.0331 ％ 0.4000 35.8422 35.4857 ％ 0.5000 36.8623 36.5939 ％ 0.6000 37.6650 37.3759 ％ 0.7000 38.2581 38.0491 ％ 0.8000 38.9390 38.7058 ％ 0.9000 39.5218 39.3437 (MATLAB implementation of SPIHT (without A rithmatic coding stage). Performance evalua% tion : this toolbox VS. SPIHT%% Test image : lena512.raw%% bpp SPIHT (dB) This code (dB)% 0. 1000 29.8107 32.7202 0.2000 29.3202% 32.2514% 0.3000 0.4000 34.5479 34.0331% 35.8422 35.485 7% 0.5000 0.6000 36.8623 36.5939% 37.6650 37.3 759% 38.0491% 0.7000 0.8000 38.2581 38.9390 38 .7058 0.9000 39.5218 39.3437%)