Transcript Artificial Neural Networks URI BME Aleksey Gladkov Introduction An

Artificial Neural Networks
URI BME
Aleksey Gladkov
Introduction
An artificial neural
network is a
mathematical or
computational
model that
approximates the
structure or function
of biological neural
networks.
(Pictured: Alvin)
Artificial Neurons
Artificial Neurons are
modeled using a function
which responds to various
weighted inputs, and is
capable of adjusting the
weights over time as it
“learns”.
Uses
Artificial neural networks can be used to
model complex relationships between many
variables, as well as being able to spot
patterns in a large quantities of data.
Drawbacks
A major problem with artificial neural
networks is the amount of work that must be
put into the “learning” step of development.
Another issue is the amount of processing
and storage required to maintain such a
network with the currently available
technology.
Applications
-Facial Recognition
-Manufacturing Process and Quality Control
-Handwriting and Speech Recognition
-Spam Filtering
-Gene Recognition
-Many Kinds of Forecasting
-Physical System Modeling
Memresistors
A memresistor is a two terminal device which
changes its resistive properties depending on the
direction of current passing through the device. The
most important feature of this substance is the ability
to retain its resistive properties even when there is no
current present.
Memristor theory was formulated and named by Leon
Chua in a 1961 paper.
In 2008 HP Labs announced the development of a
switching memristor based on a thin film of titanium
dioxide.
What This Means
Solid-state memristors can
be combined into devices
called crossbar latches,
which could replace
transistors in future
computers, taking up a much
smaller area.
HP prototyped a crossbar
latch memory using the
devices that can fit 100
gigabits in a square
centimeter.
Fuzzy Logic
Unlike Binary Logic, which has exact
values for TRUE and FALSE (0,1
respectively), Fuzzy Logic has been
extended to handle the concept of partial
truth, where the truth value may range
between completely true and completely
false.
What This Means
Fuzzy logic is a lot
more realistic than
binary logic, so it
can make the
artificial neuron
algorithm more
realistic and lifelike.
Current Research
At this time, researchers are attempting to
evaluate the effects of neuromodulators on
natural neural networks in order to better
understand their functions, which will,
hopefully, give us some insight on better
simulating them with artificial ones.
The Future of Artificial Neural
Networks
With further development of memresistor
technology and miniaturization, and liberal
application of fuzzy logic, artificial neural
networks can accurately mimic the
functions of natural ones. After that is only a
matter of time before computers that learn
as the go.
The End?
References
Alyuda. 24 Sep. 2011. <http://www.alyuda.com/products/forecaster/neural-network-applications.htm>.
Gershenson, Carlos. "Artificial Neural Networks for Beginners." Cornell University. 24 Sep.
2011.<http://arxiv.org/ftp/cs/papers/0308/0308031.pdf>.
Miller, Michael J. "Memristors: A Flash Competitor that Works Like Brain Synapses." 2010. Forward Thinking. 26 Sep. 2011.
<http://forwardthinking.pcmag.com/chips/282616-memristors-a-flash-competitor-that-works-like-brainsynapses#fbid=x6JCeArNPkc>.
Regine. "BRAINWAVE: Common Senses." 26 Sep. 2011. <http://www.we-make-money-notart.com/archives/2008/03/brainwave-common-senses.php>.
Stergiou, Christos and Dimitrios Siganos . "NEURAL NETWORKS ." Imperial College London. 24 Sep.
2011.<http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html>.
Wikipedia. 24 Sep. 2011. <http://en.wikipedia.org/wiki/Artificial_neural_network>.
All Images Courtesy of Google Images