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这个MATLAB例程是系统误码率变化仿真(The MATLAB routine changes in the system simulation of bit error rate)

说明：  经典的遗传算法工具包. 可以实现用户需要的多数遗传算法操作功能.(Classical genetic algorithm toolkit.Enables users need to operate most of the genetic algorithm function.)

基于matlab+gui的电力系统节点导纳矩阵形成程序实例，很实用( matlab gui)

This program used for spectral analysis of building according earthquake code 2800 of Iran.

用matlab编程，RBF用聚类方法求中心时的程序(Using matlab programming, RBF centers seeking clustering method using the procedure)

误差系统同步，matlaB程序，混沌系统(Synchronization error system, matlaB procedures, chaotic systems)

说明：  QPSK的Matlab/Simulink的调制解调仿真系统，给出接收信号眼图及系统仿真误码率，包含载波恢复，匹配滤波，定时恢复等重要模块，帮助理解QPSK的系统(QPSK Matlab/Simulink simulation system modulation and demodulation, the received signal is given eye and BER simulation systems, including carrier recovery, matching filter, timing recovery and other important module to help understand the system QPSK)

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

Code to find the speech beginning using rabiner method

The latest Matlab releases (starting with R2007a) include support for multiple cores. However, Matlab will only take advantage of multiple cores in evaluating certain computations like an FFT or a FIR filtering operation. Matlab will never be able to determine if, for example, consecutive function calls in a for-loop are independent of each other.