An instructional document for performing convolution and deconvolution using the MATLAB tool. It has examples.
a good descriptive guide book with matlab coding for neural networkFull description
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Descrição: a good descriptive guide book with matlab coding for neural network
Four's Stage Project: Submitted to the Sulaimani University - College of Science - Department of Physics Supervised by Dr. Omed Ghareb Abdullah
a good descriptive guide book with matlab coding for neural networkFull description
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a good descriptive guide book with matlab coding for neural network
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Para aprender erlang
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EXPERIMENT – 3 AIM:
To study the Erlang C formula form ula for probability of call blocking using MATLAB.
THEORY:
The Erlang C formula expresses the probability probability that an an arriving customer will need to be queued as opposed to immediately being served. Just as the Erlang B formula, Erlang C assumes an infinite population of sources, which jointly offer traffic of ‘A’ erlangs to ‘N’ servers. However, if all the servers are busy busy when a request arrives from a source, the request is queued. A queue is used to hold all requested calls which cannot be immediately assigned a channel. An unlimited number of requests may be held in the queue in this way simultaneously. simultaneously. This formula calculates the probability probability of queuing offered traffic, assuming that blocked calls stay in the system until they can be handled. This formula is used to determine the number of agents or customer service representatives needed needed to staff a call centre, for a specified desired probability probability of queuing. The Erlang C formula is given by:
!
= ∑
!
− +
!
−
Where, • • •
A = total traffic offered (in erlangs) N = numb number er of of serv server ers s PW = probability probability of waiting waiting
The assumptions made in Erlang C formula are similar to those used for Erlang B formula, except for the additional stipulation that if an offered call cannot be assigned a channel, it is placed in a queue which has an infinite length. Each call is then serviced in the order of its arrival arrival and the arrival arrival process obeys obeys a Poisson distribution. A common use for Erlang C is in modelling and dimensioning dimensioning call center agents in a call call center environment. MATLAB CODE:
Function Code:
function erc = erlang_c1(A,N) X = (power(A,N)/factorial(N))*(N/(N-A)); sum = 0; for k=0:N-1 d1 = power(A,k)/factorial(k); sum = sum + d1; end Y = sum + X; erc = X/Y;
Main Code:
clc; clear all; close all; N = [1:10 15 20:10:100]; A = logspace(-1,2,100); for i=1:length(N) for j=1:length(A) Pw(i,j)= erlang_c1(A(j),N(i)); end end loglog(A,Pw); axis ([0.1 100 0.001 0.1]); xlabel('Total traffic (A)' ); ylabel('Probability of waiting (Pw)' ); grid on;
GRAPH:
-1
10
) w P ( g n i t i a w -2 f 10 o y t i l i b a b o r P
