Artificial neural networks and Markov Processes

Question

I read a little about ANN and Markov process. Can someone please help me in understanding where exactly Markov process fits in with ANN and genetic algorithms. Or simply, what could be the role of Markov processes in this scenario.

Thanks alot

Answer 1

Markov process and Artificial Neural Networks are completely different concepts.

Markov processes describes any events that follow a certain statistical property. The same way the words "Gaussian" or "Random" describes a certain set of events in terms of its statistical property.

Artificial Neural Network is an algorithm that helps solve you problems, and is not really related to Markov processes. You could be thinking of Hidden Markov Models which is also an algorithm. HMM's assumes the underlying system is a Markov Process with hidden states.

Answer 2

The accepted answer is correct, but I just wanted to add a couple of details.

A Markov process is any system that goes through a series of states randomly in such a way that if you know the current state, you can predict the likelihood of each possible next states. A common example is the weather; if it's sunny now, you can predict that it is likely to be sunny later, regardless of previous weather.

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.