Stochastic neural networks are a type of artificial neural networks, which is a tool of artificial intelligence. They are built by introducing random variations into the network, either by giving the network's neurons stochastic transfer functions, or by giving them stochastic weights. This makes them useful tools for optimization problems, since the random fluctuations help it escape from local minimums. A neural network is an interconnected group of neurons. ... Artificial intelligence (also known as machine intelligence and often abbreviated as AI) is intelligence exhibited by any manufactured (i. ... The artificial neuron (also called node) is the basic unit of an artificial neural network, simulating a biological neuron. ... In the mathematics of probability, a stochastic process can be thought of as a random function. ... In the mathematics of probability, a stochastic process can be thought of as a random function. ... In mathematics, the term optimization refers to the study of problems that have the form Given: a function f : A R from some set A to the real numbers Sought: an element x0 in A such that f(x0) ≤ f(x) for all x in A (minimization) or such that... A graph illustrating local min/max and global min/max points In mathematics, a point x* is a local maximum of a function f if there exists some ε > 0 such that f(x*) ≥ f(x) for all x with |x-x*| < ε. ...

Stochastic neural networks that are built by using stochastic transfer functions are often called Boltzmann machine. A Boltzmann machine is a type of stochastic recurrent neural network originally invented by Geoffrey Hinton and Terry Sejnowski. ...