对数正态pdf

说明：  关于径向基函数神经网络、两个典型的反馈神经网络Hopfield网络、Elman网络的简单示例，适合初学者掌握径向基函数网络和反馈网络的神经网络工具箱函数，为全面学习这两类网络打下基础。(The simple examples of radial basis function neural network, two typical feedback neural networks Hopfield network and Elman network are suitable for beginners to master the neural network toolbox functions of radial basis function network and feedback network, which lays a foundation for comprehensive learning of these two types of networks.)

交互式多模滤波器的程序，可广泛应用于目标跟踪(IMM_filter)

刘利生所著外弹道测量数据处理一书的电子版，比本站版本更为清晰的版本。(Exterior ballistic measurement data processing，author liu li sheng,2001.)

基于Sage-Husa自适应卡尔曼滤波器的海浪磁场噪声抑制算法以及海浪磁场噪声的产生(Sage-Husa Adaptive kalman Filter applicated in the suppression of magnetic fields noise induced by ocean waves)

Interval Arithmetic Toolbox for Matlab

通过PWM方式直接控制逆变器的输出电流来实现静止无功补偿 也即电流直接控制法(Through PWM, the output current of the inverter is directly controlled to realize the static reactive power compensation, which is the direct current control method.)

this is algorithm for math karnel....

应用Matlab产生各种数字调制信号以及在AWGN下的传输结果。包括AM，QPSK，BPSK等(Application of Matlab have a variety of digital modulation signal and transmit the results under AWGN. Including AM, QPSK, BPSK, etc.)

免疫算法的matlab程序，对读者很有用哦，希望对大家有很大帮助，呵呵(Immune algorithm matlab procedure, useful to readers Oh, I hope all of you helps a lot, huh, huh)

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