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人脸识别中TensorLDA算法的matlab程序，使用最近邻分类器进行识别。(Face Recognition TensorLDA algorithm matlab procedures, the use of nearest neighbor classifier to identify.)

说明：  神经网络 侯媛彬 西安电子科技大学出版社 全书程序夹CH5(Neural network)

射线跟踪算法的MATLAB程序。仿真了光场传播，用差分三角形式优化了射线跟踪算法。(Ray tracing algorithm MATLAB program.)

Addition of AWGN noise using matlab function

基于buck电路的滑模控制，主要包括仿真源码和文献资料等。(Sliding mode control based on buck circuit)

说明：  含分布式电源的半不变量法概率潮流计算程序(Probabilistic Power Flow Computing Program Based on Semi-invariant Method)

自适应抵消与谱相减结合的单通道语音消噪算法的研究，对这方面的研究比较详细(Adaptive cancellation and spectral subtraction with the single-channel speech denoising algorithm, more detailed research in this area)

LMS algorithm for adaptive filtering. Worth seeing.

说明：  按照信息论基本原理的解释，信息是系统有序程度的一个度量，熵是系统无序程度的一个度量；如果指标的信息熵越小，该指标提供的信息量越大，在综合评价中所起作用理当越大，权重就应该越高。因此，可利用信息熵这个工具，计算出各个指标的权重，为多指标综合评价提供依据。(According to the interpretation of the basic principles of information theory, information is a measure of the degree of orderliness of the system, and entropy is a measure of the degree of disorder of the system. If the information entropy of the index is smaller, the amount of information provided by the index is larger, and the role of the index in the comprehensive evaluation should be greater, the weight should be higher. Therefore, we can use the tool of information entropy to calculate the weight of each index, and provide a basis for the comprehensive evaluation of multiple indicators.)

Prueba1 FletcherReeves Hernández Santiago José Maestría en Ciencias de la Computación Septiembre / 2011 1. Comenzar con un punto arbitrario 2. Calcular Gradiente de Fi 3. Si el Gradiente Fi es igual a 0(converge), termina 4. Si el Gradiente Fi es !=0 continuar 5. Encontrar dirección de búsqueda Si= -GradienteFi= - Gradiente F(Xi) 6. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Hacer i=2 8. Obtener Gradiente Fi 9. Calcular Si= -GradFi + ( [abs(GradFi)^2]/[abs(GradF(i-1))^2] )*S(i-1) 10. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Verificar Optimalidad de X(i+1) Si es optimo, detener Si no es optimo hacer i=i+1 e ir al paso 8