A Matlab code to plot the matched filter for 16-element linear array with constant phase weights on transmit and receive LFM waveform parameters.計算具有收發線性調頻波形參數的相位權重的16單元線性匹配濾波器
In some graphs, the shortest path is given by optimizing two different metrics: the sum of weights of the edges and the number of edges. For example: if two paths with equal cost exist then, the path with the least number of edges is chosen as the shortest path. Given this metric, you have find out the shortest path between a given pair of vertices in the input graph. The output should be the number of edges on the path, the cost of the shortest path, and the path itself. Input is the adjacency matrix and the two vertices.
LVQ學習矢量化算法源程序
This directory contains code implementing the Learning vector quantization
network. Source code may be found in LVQ.CPP. Sample training data is found
in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
LVQ program accepts input consisting of vectors and calculates the LVQ
network weights. If a test set is specified, the winning neuron (class) for
each neuron is identified and the Euclidean distance between the pattern and
each neuron is reported. Output is directed to the screen.
This Program Is Designed To Simulate A Spatial Antenna Array
System Working On The MUSIC Algorithm For The Angle Of Arrival Estimation
And Null Steering Algorithm For The weights Estimation To The Required
Output Radiation Pattern .
This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
Returns weighted percentiles of a sample given the weight vector w
％ The idea is to give more emphasis in some examples of data as compared to
％ others by giving more weight. For example, we could give lower weights to
％ the outliers.
％ The motivation to write this function is to compute percentiles for Monte
％ Carlos simulations where some simulations are very bad (in terms of goodness
％ of fit between simulated and actual value) than the others and to give
％ the lower weights based on some goodness of fit criteria.
％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％
This function calculates Akaike s final prediction error
％ estimate of the average generalization error.
％
％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the
％ final prediction error estimate (fpe), the effective number of
％ weights in the network if the network has been trained with
％ weight decay, an estimate of the noise variance, and the Gauss-Newton
％ Hessian.
％
This function calculates Akaike s final prediction error
％ estimate of the average generalization error for network
％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive
％ counterparts.
％
％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat)
％ produces the final prediction error estimate (fpe), the effective number
％ of weights in the network if it has been trained with weight decay,
％ an estimate of the noise variance, and the Gauss-Newton Hessian.
％
