CE803-Matlab-Tutorials

quarter car animation

Source Code in Matlab implementing Source Separation as used in Bell and Sejnowski 1996.An information maximisation approach to blind separation and blind deconvolution.

粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法。 BP（Back Propagation）神经网络是1986年由Rumelhart和McCelland为首的科学家小组提出，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(hidden layer)和输出层(output layer)。(Particle swarm optimization, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years (Evolutionary Algorithm- EA). Kind, and simulated annealing algorithm PSO algorithm is similar evolutionary algorithms, it is also starting a random solution, through an iterative search for the optimal solution, which is also used to uate the quality through fitness solution, but it is simpler than genetic algorithm rules It has no genetic algorithm " crossover" (Crossover) and " variant" (Mutation) operation, which by following the current search to find the optimal value to the global optimum. This algorithm is its easy implementation, high accuracy, fast convergence, etc. attracted academic attention and show its superiority in solving practical problems. PSO algorithm is a parallel algorithm. BP (Back Propagation) neural network is a 1986 team of scientists headed by Rumelhart and McC)

现代信号处理丛书，概述详细介绍了现代信号处理的方法，配有很多案例，对信号专业的学生很有帮助.(Modern Signal Processing Series, outlined details of the methods of modern signal processing, with a lot of cases, the signal majors helpful.)

随机森林分类代码，，很详细，，含有含多个函数文件(Random forest classification code, very detailed, containing containing multiple functions file)

基于Matlab的MIMO通信系统仿真，使用最大似然译码准则。适合初学者使用(Simulation of MIMO communication system based on Matlab, using the maximum likelihood decoding criterion. Suitable for beginners to use )

IRIS Recognition code

Effi cient algorithms for signal processing are critical to very large scale future appli- cations such as video processing and four-dimensional medical imaging. Similarly, effi cient algorithms are important for embedded and power-limited applications since, by reducing the number of computations, power consumption can be reduced con- siderably. This unique textbook presents a broad range of computationally-effi cient algorithms, describes their structure and implementation, and compares their relative strengths. All the necessary background mathematics is presented, and theorems are rigorously proved. The book is suitable for researchers and practitioners in electrical engineering, applied mathematics, and computer science.

说明：  对输入的信号计算其的分形维数，用以判断信号的复杂度(The input signal to calculate the fractal dimension of complexity to determine the signal)

We have discussed and analyzed the case of no noise uncertainty. Now, considering the case with uncertainty in the noise model [20], the distributional uncertainty of noise can be represented