Stochastic neural networks for modelling random processes from observed data

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Title

Stochastic neural networks for modelling random processes from observed data

Content partner

Lincoln University

Collection

Lincoln University Research Archive

Description

Most Artificial Neural Networks that are widely used today focus on approximating deterministic input-output mapping of nonlinear phenomena, and therefore, they can be well trained to represent the average behaviour of a nonlinear system. However, most natural phenomena are not only nonlinear but also highly variable. Deterministic neural networks do not adequately represent the variability observed in the natural settings of a system and therefore cannot capture the complexity of the whole s...

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Format

Research Paper

Research format

Book item

Date created

2016-02-01

Creator

Ling, H / Samarasinghe, Sandhya / Kulasiri, Don

URL

https://hdl.handle.net/10182/12586

Related subjects

Stochastic neural networks / Random processes / Karhunen-Loeve Theorem / White noise / Biological and environmental systems / Control engineering, mechatronics and robotics / Artificial intelligence / Machine learning

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