bstev@pnet12.cts.com (Barry Stevens) (10/05/88)
Two weeks ago, I posted a message that I had done a survey of companies
looking for applications that were suitable for neural networks, and asking
if there was any interest. Since that time, the responses have been coming
back by Email almost daily, just over 30 of them so far.
Accordingly, I have prepared a summary of that study for posting in
comp.neural-nets. The summary appears as a comment to this message, and is
approximately 200 lines in length.
The original report can't be released, since it contains some proprietary
material. The summary, however, contains material that is available from
numerous public sources, if one knew where and when to look. To keep the
post down, I had to exclude all of the technical detail that usually
accompanies discussion about neural networks. I describe the applications
that were identified, and what, if anything, has been done about them.
Period.
Barry Stevens
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INET: bstev@pnet12.cts.combstev@pnet12.cts.com (Barry Stevens) (10/05/88)
COMMERCIAL APPLICATIONS OF NEURAL NETWORKS
Barry A. Stevens
Copyright 1988 Applied AI Systems, Inc.
We recently completed a survey of a number of organizations looking for
commercial applications for neural networks. The study was done for a
vendor of neural network hardware and software. (See the notices at the
end of this article.) Information in this summary is from that study and
also in the public domain, from literature, press releases, and
demonstrations describing neurocomputer applications.
THE SURVEY
Forty companies were contacted, and after locating the correct contact,
the characteristics of neurocomputer operation were discussed in some
detail. Operational activities of each company were then briefly reviewed,
to identify those non-engineering applications where neural networks could
either perform better than methods currently in use, or provide a
capability that did not currently exist. For four applications, data were
obtained from interested companies for test implementation by vendor
technical personnel. Information from this study, together with available
information about engineering-oriented uses of neural networks, were used
to produce the following list of viable commercial applications for neural
networks. Most, if not all, of the following data are available in the
public domain, either in written form, or as demonstrations of capability
used at trade shows.
This is a summary report, devoid of significant technical detail. It is
meant only as an overview of applications. Providing adequate detail for
even one of these applications would create a file too large to
comfortably transfer via USENET.
THE APPLICATIONS
EKG Processing
--------------
In normal EKG processing, digital filters are used to remove unwanted
signals from muscle groups other than the heart as well as background
noise from power lines and other instruments. The filters operate in the
frequency domain, and distort the signal in the process of removing the
noise. A neural network, trained to recognize both normal and abnormal EKG
patterns, identifies the features of those signals found in the noise in
the time domain. The result is better signal to noise ratio and less
signal distortion than with conventional techniques.
Process Control
---------------
A neural network was trained to solve a classic control problem --
balancing a broom mounted on a servo-controlled cart.
Standard approaches to solving process control problems involve
development of a control law, one or more functional relationships
describing the process being controlled. The control law is then tested
through simulation. The approach is time-consuming, and doesn't work well
when complicated non-linear functions are involved, or when there is no
apparent functional relationship in the process.
A neural network has the capability to observe data from a process and,
given a goal of controlling a variable in that process, of learning the
requisite control law. In the case of the broom-balancing application, a
television camera was used as a position sensor for the broom. A neural
network processed the image position data, and velocity and acceleration
information derived from them. By observing these data during attempts to
balance the broom, the network eventually learned a control law to balance
it, using a learning algorithm proprietary to the vendor.
The neural network approach works particularly well in complicated, non-
linear situations or where there appears to be no functional relationships
to use in development of a control law.
Reading Hand Printed Numbers
----------------------------
A neural network has been successfully trained to recognize handprinted
characters by exposing a network to video or graphic tablet samples of
numerics printed by a number of people. The trained network has been able
to successfully identify wide variations in characters, including
superimposed characters such as an "8" written inside a "0", distorted
characters, or characters composed of broken or dotted lines.
Inspection and Quality Control
------------------------------
Neural networks have been trained to recognize the positioning of labels
on bottles of food, beers, soda, or medicines. Video input is used, and
the network is trained to recognize the labels being viewed.
Neural networks are also ideally suited to assembly line testing, where
automated test equipment gathers information about an assembly, such as a
car, or an electronic device, on a periodic basis. The data can be
gathered, and displayed on a PC for a human quality control expert. The
data, and the resulting judgment of the human expert, can be captured and
used as training data for a neural network. When enough training data
become available, the network will be capable of making judgments as the
human expert did.
Recognition of a Face in a Video Image
---------------------------------------
A neural network has been successfully used to recognize the fourier
transformation of a facial image within the fourier transformation of a
video image containing that face. The resulting identification of faces
has proven to be somewhat insensitive to facial position, presence or
absence of a smile, or presence or absence of eyeglasses.
Consumer Loan Credit Screening
------------------------------
Data from 17,000 completed consumer loans have been used to train and test
a neural network as a loan officer. The network was trained using the
information on 10,000 of the consumer loan application together with a
grade for the payment history on them. Tests on 7,000 of the loans not
used during training indicate that if the network was used to screen
loans, instead of the expert system currently used, a 27% increase in
profitability would have been experienced. The rule-based expert system
took two man years to build. The neural network was trained in three
weeks.
Mortgage Processing
-------------------
Banks have identified mortgage screening as another fruitful area for
neural networks. This is essentially the same as consumer loan credit
screening, except that significantly more information from more sources is
used in the process. Neural networks have the ability to learn from the
judgment of human loan officers. The application, credit report, and
character check may be combined and presented to the network, together
with the judgment of those items made by the loan officer. As more cases
are captured and used to train the network, it gradually learns to make
the decisions in the same manner as the loan officer, even if such
decisions are subjective, even irrational.
Insurance Claims Processing
---------------------------
Insurance companies need to process historical claim data and determine
if:
current claims submitted may be fraudulent; or
how much money should be held in reserve to pay submitted claims.
By using historical claim information, together with information on the
disposition of the claims as well as the amounts actually paid, the
required network training can be accomplished.
Stock or Commodity Trading
--------------------------
Neural networks have been successfully used to learn features present in
commodity price data over time, and to identify those features when
confronted with real-time data containing those features. Thirty-three
features were learned including such classics as head-and-shoulders,
double tops, flags, and s-curves, among others. It was not necessary to
know the nature of the features ahead of time; the network learned
whatever features were present. Once features were learned, information on
the sequence of features was determined statistically, and used to develop
a trading strategy.
Inventory Control
-----------------
A Fortune-500 company uses a large computer-based inventory control system
to process order, inventory, and shipment data for 40,000 customers, 2,000
vendors, and 1,000,000 SKU items. Approximately 100 parameters control
every aspect of the operation of the inventory system. It is desired to
use those parameters to minimize the amount of inventory remaining on
hand. No one has been able to solve the resulting minimization problem
using conventional inventory management techniques.
The inventory system contains a simulation model. The model, using an
audit trail of all inventory activity, is capable of simulating many
months' business under the control of the same parameters that control the
main inventory system. A neural network can be used together with a rule-
based expert system to perform a global search for a minimum value of
inventory, following these steps:
systematically change the parameters;
run the simulation;
observe the remaining inventory;
decide if the change is good;
continue searching in the same "direction" if it was a good change;
change directions if the change was bad.
The neural network quickly learns the relationship between the control
parameters and the objective of minimizing the amount of inventory.
Reading Text
------------
Neural networks can be used to recognize text, rather than parse it,
saving considerable amounts of processing time.
In a large datacenter, as one example, with multiple IBM mainframe CPUs,
operator console and telecommunications console messages must be responded
to by human operators. The messages are generated at a rate of from 5 to
50 *per second*. This is well beyond the rate at which human operators can
function. One solution is to identify the messages automatically, and
allow an expert system to take automatic action where possible. This will
reduce the workload for human operators. Identification of the messages
using conventional techniques requires a significant amount of processing
time. IBM sells a product which performs the function but, wouldn't you
know it, requires a small mainframe to run.
A neural network can be taught to classify console messages for a
companion expert system. Once a network is trained, classification occurs
in one pass through the neural network, providing very fast response. The
companion expert system then uses the classification to extract and act on
information found in the message.
THE NETWORKS USED
In almost all cases where networks were implemented to test the
applications described above, the back propagation network was used. In a
few cases, a vendor-proprietary learning algorithm and network were used.
Test applications were implemented on an IBM/AT class machine with the
ANZA neural network co-processor board sold by HNC, Inc.
THE SURVEY PROJECT
The project was paid for by HNC, Inc., of San Diego, CA. Personnel from
both firms worked on the various stages of the applications that were
tested.
CONTACT
Barry A. Stevens may be reached at:
Applied AI Systems, Inc.
PO Box 2747
Del Mar, CA 92014
619-755-7231
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