winforms pdf 417

winforms pdf 417

winforms pdf 417

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A search for the global optimum is conducted in the parameter space surrounding the candidate solution, either deterministically (eg, grid search) or randomly As with the search for starting values, there is no good nite number of parameter vectors or bounds on the parameter space that will guarantee that the global optimum will be identi ed This is particularly true for the grid search approach The grid search is best applied when the researcher suspects that the search algorithm stopped prematurely at a local optimum atop a at region around the global maximum, a situation that Gill and King (2000) refer to as locating the highest piece of broken glass on top of a hill In this case the grid search neighborhood has a clear bound, the at region around the candidate solution, that the researcher can employ in searching for the global maximum.

strategic concepts and ideas formulated at the corporate or business level and the role of the project manager at a lower functional or operating level. When I address business strategy, I am also including the alignment of information technology as an integral part of the game plan. The reason may be that companies that are reluctant to invest in new technologies may therefore never address their IT problems, or worse, are left behind by their competitors. Therefore, every organization needs a documented strategy that is realistic and is agreed to by everyone. Good strategy leads to good results. Bad strategy will not allow an organization to survive its competition. Let me illustrate an example of how technology and market trends are forcing organizations to adapt their business strategies to meet future IT demands. It is estimated that by 2005 over 80 million people will be sending wireless images on the fly, using numerous digital devices. Sounds like something from Star Trek, doesn't it If this prediction comes true, then existing network infrastructures run the serious risk of becoming outdated, as newer high-speed networks on the 128 kbps and 384K Time Multiple Access Division (TDMA) range will be needed to handle these technologies. Many companies will therefore need to revise their business and IT strategies, and project managers will be required to implement these resulting new strategies (see Figure 1.1).

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When reading the Reactor pattern description in isolation, however, it is not obvious how to apply Active Object or Monitor Object effectively in the presence of Acceptor-Connector, which may also use these patterns. For example, the Reactor pattern does not specify whether or not a Reactor should apply Active Object to implement a particular type of event handler. Nor does it specify whether an Acceptor-Connector that uses the Reactor should use Monitor Object to implement its acceptors, connectors, and service handlers. In general, not all possible combinations of these four patterns are useful. However, because each pattern description is self-contained and independent of the others, it is hard to extract the useful combinations from the individual pattern descriptions.

1 The peak values occur at times t = p+ T 2 2 where p is an integer and T = is voltage period (T = 20 ms for f = 50 Hz), thus:

Drawing on a result presented by de Haan (1981), Veall (1990), suggests that by using a random search and applying extreme asymptotic theory, a con dence interval for the candidate solution can be formulated According to Veall (1990, p 1460) the method is to randomly choose a large number, n, of values for the parameter vector using a uniform density over the entire parameter space Call the largest value of the evaluated likelihood function L1 and the second largest value L2 The 1 p con dence interval for the candidate solution, L , is [L1 , Lp ], where Lp = L1 + L1 L2 p 1/ 1 (420).

%_______________________________________________________ % Iterate through generations while iga<maxit iga=iga+1; % increments generation counter %_______________________________________________________ % Pair and mate pick1=ceil(Nodds*rand(1,M)); % mate #1 pick2=ceil(Nodds*rand(1,M)); % mate #2 % ma and pa contain the indicies of the parents ma=odds(pick1); pa=odds(pick2); %_______________________________________________________ % Performs mating for ic=1:M mate1=pop(ma(ic),:); mate2=pop(pa(ic),:); indx=2*(ic-1)+1; % starts at one and skips every % other one xp=ceil(rand*npar); % random value between 1 and N temp=mate1; x0=xp; while mate1(xp)~=temp(x0) mate1(xp)=mate2(xp); mate2(xp)=temp(xp); xs= nd(temp==mate1(xp)); xp=xs; end pop(keep+indx,:)=mate1; pop(keep+indx+1,:)=mate2; end %_______________________________________________________ % Mutate the population nmut=ceil(popsize*npar*mutrate); for ic = 1:nmut row1=ceil(rand*(popsize-1))+1; col1=ceil(rand*npar); col2=ceil(rand*npar);

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