By E.S. Gopi
The Algorithms akin to SVD, Eigen decomposition, Gaussian mix version, HMM and so forth. are shortly scattered in several fields. There continues to be a necessity to gather all such algorithms for speedy reference. additionally there's the necessity to view such algorithms in program perspective. This publication makes an attempt to meet the above requirement. The algorithms are made transparent utilizing MATLAB courses.
The Algorithms resembling SVD, Eigen decomposition, Gaussian blend version, HMM and so on. are scattered in numerous fields. there's the necessity to gather all such algorithms for speedy reference. additionally there's the necessity to view such algorithms in software viewpoint. set of rules Collections for electronic sign Processing functions utilizing MATLAB makes an attempt to fulfill the above requirement. additionally the algorithms are made transparent utilizing MATLAB courses.
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Additional resources for Algorithm Collections for Digital Signal Processing Applications using Matlab
Desired ouput vector is represented is [t1 t2] The vectors are related as follows. 1 Single Neuron Architecture Consider the single neuron input layer and single neuron output layer. Output= func (input * W +B) Desired ouput vector be represented as ‘Target’ Requirement is to find the optimal value of W so that Cost function = (Target-Output)2 is reduced. The graph plotted between the weight vector ‘W’ and the cost function is given in the figure 1-11. Optimum value of the weight vector is the vector corresponding to the point 1 which is global minima point, where the cost value is lowest.
As ‘newcost’> ‘curcost’, ‘newval’ is inferior compared with ‘curval’ in minimizing the cost function. As the temperature (T) is large and exp((curcost-newcost)/T)>rand, ‘newcost’ value is assigned to ‘curcost’ and ‘newval’ is assigned to ‘curval’. 2171=1/log(100), where 100 is the number of iterations used. This is the thumb rule used in this example. This process is said to one complete iteration. Next randomly selected value is selected and the above described process is repeated for 100 iterations.
1. Artificial Intelligence 21 Figure1-7 Illustration of Simulated Annealing 1 As iteration increases the simulated temperature is decreased and the value selected for the variable ‘x is moving towards the global minima point as shown in the figure 1-7. Thus values assigned to the variable ‘x’ for the first few iterations (about 10 iterations in this example) is not really in decreasing order of f(x). This helps to search the complete range and hence helps in overcoming the problem of local minima.
Algorithm Collections for Digital Signal Processing Applications using Matlab by E.S. Gopi