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Artificial Neural Networks can be viewed as a
mathematical model to simulate natural and
biological systems on the basis of mimicking the
information processing methods in the human brain.
The capability of current ANNs only focuses on
approximating arbitrary deterministic input-output
mappings. However, these ANNs do not adequately
represent the variability which is observed in the
systems natural settings as well as capture the
complexity of the whole system behaviour. This
thesis addresses the development of a new class of
neural networks called Stochastic Neural Networks
in order to simulate internal stochastic
properties of systems. Developing a suitable
mathematical model for SNNs is based on canonical
representation of stochastic processes or systems by
means of Karhunen-Loève Theorem. Some successful
real examples, such as analysis of full displacement
field of wood in compression, confirm the validity
of the proposed neural networks. Furthermore,
analysis of internal workings of SNNs provides an in-
depth view on the operation of SNNs that help to
gain a better understanding of the simulation of
stochastic processes by SNNs.
mathematical model to simulate natural and
biological systems on the basis of mimicking the
information processing methods in the human brain.
The capability of current ANNs only focuses on
approximating arbitrary deterministic input-output
mappings. However, these ANNs do not adequately
represent the variability which is observed in the
systems natural settings as well as capture the
complexity of the whole system behaviour. This
thesis addresses the development of a new class of
neural networks called Stochastic Neural Networks
in order to simulate internal stochastic
properties of systems. Developing a suitable
mathematical model for SNNs is based on canonical
representation of stochastic processes or systems by
means of Karhunen-Loève Theorem. Some successful
real examples, such as analysis of full displacement
field of wood in compression, confirm the validity
of the proposed neural networks. Furthermore,
analysis of internal workings of SNNs provides an in-
depth view on the operation of SNNs that help to
gain a better understanding of the simulation of
stochastic processes by SNNs.