35 research outputs found
Performance of Chaos and Burst Noises Injected to the Hopfield NN for Quadratic Assignment Problems
In this paper, performance of chaos and burst noises injected to the Hopfield Neural Network for quadratic assignment problems is investigated. For the evaluation of the noises, two methods to appreciate finding a lot of nearly optimal solutions are proposed. By computer simulations, it is confirmed that the burst noise generated by the Gilbert model with a laminar part and a burst part achieved the good performance as the intermittency chaos noise near the three-periodic window
Neuromorphic Engineering Editors' Pick 2021
This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors
Low Power Memory/Memristor Devices and Systems
This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Proceedings of AUTOMATA 2010: 16th International workshop on cellular automata and discrete complex systems
International audienceThese local proceedings hold the papers of two catgeories: (a) Short, non-reviewed papers (b) Full paper
Dynamics of Macrosystems; Proceedings of a Workshop, September 3-7, 1984
There is an increasing awareness of the important and persuasive role that instability and random, chaotic motion play in the dynamics of macrosystems. Further research in the field should aim at providing useful tools, and therefore the motivation should come from important questions arising in specific macrosystems. Such systems include biochemical networks, genetic mechanisms, biological communities, neutral networks, cognitive processes and economic structures. This list may seem heterogeneous, but there are similarities between evolution in the different fields. It is not surprising that mathematical methods devised in one field can also be used to describe the dynamics of another.
IIASA is attempting to make progress in this direction. With this aim in view this workshop was held at Laxenburg over the period 3-7 September 1984. These Proceedings cover a broad canvas, ranging from specific biological and economic problems to general aspects of dynamical systems and evolutionary theory
Form vs. Function: Theory and Models for Neuronal Substrates
The quest for endowing form with function represents the fundamental motivation behind all neural network modeling. In this thesis, we discuss various functional neuronal architectures and their implementation in silico, both on conventional computer systems and on neuromorpic devices. Necessarily, such casting to a particular substrate will constrain their form, either by requiring a simplified description of neuronal dynamics and interactions or by imposing physical limitations on important characteristics such as network connectivity or parameter precision. While our main focus lies on the computational properties of the studied models, we augment our discussion with rigorous mathematical formalism. We start by investigating the behavior of point neurons under synaptic bombardment and provide analytical predictions of single-unit and ensemble statistics. These considerations later become useful when moving to the functional network level, where we study the effects of an imperfect physical substrate on the computational properties of several cortical networks. Finally, we return to the single neuron level to discuss a novel interpretation of spiking activity in the context of probabilistic inference through sampling. We provide analytical derivations for the translation of this ``neural sampling'' framework to networks of biologically plausible and hardware-compatible neurons and later take this concept beyond the realm of brain science when we discuss applications in machine learning and analogies to solid-state systems
Bacteria classification with an electronic nose employing artificial neural networks
This PhD thesis describes research for a medical application of electronic nose technology.
There is a need at present for early detection of bacterial infection in order to
improve treatment. At present, the clinical methods used to detect and classify bacteria
types (usually using samples of infected matter taken from patients) can take up to
two or three days. Many experienced medical staff, who treat bacterial infections, are
able to recognise some types of bacteria from their odours. Identification of pathogens
(i.e. bacteria responsible for disease) from their odours using an electronic nose could
provide a rapid measurement and therefore early treatment. This research project used
existing sensor technology in the form of an electronic nose in conjunction with data
pre-processing and classification methods to classify up to four bacteria types from
their odours. Research was performed mostly in the area of signal conditioning, data
pre-processing and classification. A major area of interest was the use of artificial neural
networks classifiers. There were three main objectives. First, to classify successfully
a small range of bacteria types. Second, to identify issues relating to bacteria odour
that affect the ability of an artificially intelligent system to classify bacteria from odour
alone. And third, to establish optimal signal conditioning, data pre-processing and
classification methods.
The Electronic Nose consisted of a gas sensor array with temperature and humidity
sensors, signal conditioning circuits, and gas flow apparatus. The bacteria odour was
analysed using an automated sampling system, which used computer software to direct
gas flow through one of several vessels (which were used to contain the odour samples,
into the Electronic Nose. The electrical resistance of the odour sensors were monitored
and output as electronic signals to a computer. The purpose of the automated sampling system was to improve repeatability and reduce human error. Further improvement
of the Electronic Nose were implemented as a temperature control system which controlled
the ambient gas temperature, and a new gas sensor chamber which incorporated
improved gas flow.
The odour data were collected and stored as numerical values within data files in
the computer system. Once the data were stored in a non-volatile manner various classification
experiments were performed. Comparisons were made and conclusions were
drawn from the performance of various data pre-processing and classification methods.
Classification methods employed included artificial neural networks, discriminant
function analysis and multi-variate linear regression. For classifying one from four
types, the best accuracy achieved was 92.78%. This was achieved using a growth phase
compensated multiple layer perceptron. For identifying a single bacteria type from a
mixture of two different types, the best accuracy was 96.30%. This was achieved using
a standard multiple layer perceptron.
Classification of bacteria odours is a typical `real world' application of the kind that
electronic noses will have to be applied to if this technology is to be successful. The
methods and principles researched here are one step towards the goal of introducing
artificially intelligent sensor systems into everyday use. The results are promising and
showed that it is feasible to used Electronic Nose technology in this application and that
with further development useful products could be developed. The conclusion from this
thesis is that an electronic nose can detect and classify different types of bacteria