Chapter 9

Probability-based Neural Network Systems

Pierre Lorrentz


Since Gaussian distribution may be employed as a universal approximator, it is clear that most modelling and optimisation problems could be solved by probabilitybased ANN systems. For this reason, chapter 9 concentrate on probability-based ANN systems. The first section introduces the random number generator, which has application in Markov-Chain and its hybrid, in subsequent sections. The fifth section describes the Restricted Boltzmann Machine (RBM) in detail. The Boltzmann machine may be a component network of Deep Belief Networks (DBN), which is described in the last section. The chapter has explained many algorithms related to DBN with great intuition, as this may facilitate better understanding and therefore implementation.

Total Pages: 171-197 (27)

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