Sha*_*zad 2 artificial-intelligence neural-network markov-models
我读了一些关于ANN和马尔可夫过程的文章.有人可以帮助我理解Markov过程在ANN和遗传算法中的确切位置.或者简单地说,马可夫过程在这种情况下可能起什么作用.
非常感谢
chm*_*chm 12
接受的答案是正确的,但我只是想补充一些细节.
马尔可夫过程是一种随机经历一系列状态的系统,如果您知道当前状态,则可以预测每种可能的下一状态的可能性.一个常见的例子是天气; 如果它现在是晴天,你可以预测它可能会在以后晴天,不管以前的天气如何.
A genetic algorithm is one that begins by generating a bunch of arbitrary random solutions to a given problem. It then checks these solutions to see how good they are. The 'bad' solutions are discarded, the 'good' solutions are kept and combined together to form (hopefully) better solutions, just like successful members of a species breeding a new generation. In theory, repeating this process will give better and better solutions until you eventually have an optimal one.
As you can see, they aren't algorithmically related. However, genetic algorithms are often used to generate Hidden Markov Models, for example here. The basic idea is that an HMM is initialized with random weights, a 'training set' of related Markov processes is run through it, and the weights are adjusted to give the members of the training set the highest probability of occurring. This is often done in speech recognition software.