### What are Neural
Networks?

Neural networks are
special computing elements that can be trained to make decisions
based on historical data. They work by discovering patterns and by
inducing rules that define empirical relationships. Some example
applications of neural networks include credit screening, stock
market forecasting, bond rating, and market segmentation.

### Are Neural Networks
Different From Expert Systems?

In rule-based expert
systems, a knowledge engineer must deduce expert rules. Neural
networks induce rules directly from historical data in the form of
pattern recognition functions. You can use these functions just like
the classification rules in an expert system.

### How do Neural
Networks Compare with Statistical Techniques?

Neural network
supervised learning corresponds to statistical non-linear
discriminant analysis; neural network unsupervised learning
corresponds to statistical clustering and factor analysis. Instead
of executing complex statistical procedures -- the neural network
learning model iteratively converges to a pattern recognition
function that is a optimal in a least-mean-square sense. The
iterative process implicitly estimates the statistics of the
historical training data. This parallels human pattern recognition:
people learn patterns over time.

### What is NNetSheet?

NNetSheet is a development system that creates
spreadsheet models of neural networks. We call these models Neural
Network Spreadsheets. NNetSheet-C implements the neural network
learning algorithms as dynamic link libraries -- C programs
dynamically linked to Microsoft Excel spreadsheets. To the
spreadsheet user, these neural network algorithms look like ordinary
spreadsheet formulas.

### How is NNetSheet Different from other Neural
Network Tools?

Our neural network tools maximize ease of use,
comprehensiveness, and portability. The neural network models and
preprocessing utilities include:

- Supervised
Learning (for Prediction)

Perceptron
Learning, Delta Rule, Generalized Delta Rule, Back
Propagation
- Unsupervised Learning (for Clustering)

Hamming Metrics, Fuzzy Metrics, Hamming Histograms,
Euclidean Metrics
- Data
Preprocessing

Randomizing, Text Input,
Scaling, Moving Averages

### How Easy is it to Build a Neural Network
Spreadsheet with NNetSheet?

If you know how to use a spreadsheet, then you
know how to use the NNetSheet. Building a neural network spreadsheet
is a three step process:

- Get the
data for your problem and specify the learning.

Import data containing your training cases. Specify the
learning or clustering required by selecting regions on the
spreadsheet with the mouse.
- Train the
Neural Network Spreadsheet.

Set the
number of epochs for training, the change tolerance, and other
parameters. Run the neural network with the Calculate Now...
spreadsheet command. The learning process is done when the
weights converge.
- Deploy and
Customize.

Deploy the Neural Network
Spreadsheet -- a spreadsheet that embeds the neural network as a
set of spreadsheet formulas. Customize it -- just like any other
spreadsheet -- and use the neural network formulas with the
NNetSheet runtime system in your
application.

(c) 2006 Inductive Solutions, Inc. All rights
reserved.