Armstrong, James R (2011) Boolean Weightless Neural Network Architectures. Doctoral thesis, University of Central Lancashire.
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A collection of hardware weightless Boolean elements has been developed. These form fundamental building blocks which have particular pertinence to the field of weightless neural networks. They have also been shown to have merit in their own right for the design of robust architectures.
A major element of this is a collection of weightless Boolean sum and threshold techniques. These are fundamental building blocks which can be used in weightless architectures particularly within the field of weightless neural networks. Included in these is the implementation of L-max also known as N point thresholding. These elements have been applied to design a Boolean weightless hardware version of Austin’s ADAM neural network. ADAM is further enhanced by the addition of a new learning paradigm, that of non-Hebbian Learning. This new method concentrates on the association of ‘dis-similarity’, believing this is as important as areas of similarity.
Image processing using hardware weightless neural networks is investigated through simulation of digital filters using a Type 1 Neuroram neuro-filter. Simulations have been performed using MATLAB to compare the results to a conventional median filter. Type 1 Neuroram has been tested on an extended collection of noise types. The importance of the threshold has been examined and the effect of cascading both types of filters was examined.
This research has led to the development of several novel weightless hardware elements that can be applied to image processing. These patented elements include a weightless thermocoder and two weightless median filters. These novel robust high speed weightless filters have been compared with conventional median filters.
The robustness of these architectures has been investigated when subjected to accelerated ground based generated neutron radiation simulating the atmospheric radiation spectrum experienced at commercial avionic altitudes. A trial investigating the resilience of weightless hardware Boolean elements in comparison to standard weighted arithmetic logic is detailed, examining the effects on the operation of the function when implemented on hardware experiencing high energy neutron bombardment induced single event effects.
Further weightless Boolean elements are detailed which contribute to the development of a weightless implementation of the traditionally weighted self ordered map.
|Item Type:||Thesis (Doctoral)|
|Uncontrolled Keywords (separate with ;):||Neural; Networks; Boolean; Logic; ADAM; Neuroram; atmospheric radiation; image filtering; self ordered map; SOM; weightless; Weightless neural networks;|
|Subjects:||Physical sciences > Physics|
Engineering > Environmental engineering
|Schools:||College of Science and Technology > School of Physical Sciences and Computing|
|Deposited By:||Khalil Ahmed Patel|
|Deposited On:||08 Jun 2012 16:09|
|Last Modified:||17 May 2016 12:32|
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