resp1

说明：  实现机器人的自动编队，并可以避开障碍物，到达目的地的过程。(Automatic robot formations, and can avoid obstacles and reach the destination process.)

OFDM信号通过功率放大器以及功率放大器的预失真全套代码(OFDM PA)

物元分析法及承载力指数验证法计算承载力源代码(Matter-element analysis and verification method to calculate the carrying capacity carrying capacity index source code)

通过仿真表明，该算法同几种经典的分布式算法相比，不但降低了发送功率，提高了用户效用，而且有较好的收敛性能，适用于对时延特性要求较高。(Through the simulation shows that this algorithm with several classic distributed algorithm, not only reduce the sending power, improve the user utility, and has better convergence performance, suitable for time delay characteristic request is higher. )

mimo-ofdm系统仿真，输出仿真图，信道脉冲响应实部虚部随信噪比变化时的改变图(mimo-ofdm system simulation, simulation output figure, the channel impulse response changes in real and imaginary parts of the map changes with SNR)

Dissolve Gas Analysis

仿真模型为Buck变换器，采用电压电流双闭环控制，输出电压稳定，无超调，鲁棒性好(Buck transformer)

说明：  pts-clipping与 pts与clipping(pts-clipping and pts and clipping)

BP（Back Propagation）网络是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。 BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。 它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。 通过对信息的提取以及记忆，用作分类、聚类、预测等。 (BP (Back Propagation) network is a pre-press error back propagation algorithm Multilayer feedforward network, is one of the most widely used neural network model. BP network can learn and store a large amount of input- output mode mapping without prior reveal mathematical equations that describe the mapping relationship. It s learning rule is to use the steepest descent method, by reverse spread to constantly adjust the network weights and thresholds and the minimum sum of squared error of the network. BP neural network topology comprises an input layer (input), hidden layer (hide layer) and an output layer (output layer). By extracting information and memory, it is used as classification, clustering, prediction.)

用matlab程序自编的数独程序，可以解决大部分的9*9宫格的数独。(Sudoku program, self matlab program can solve most of the 9 x 9 Sudoku grids.)