Page 82 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes Another aspect of the artificial neural networks is that there are different architectures, which
consequently requires different types of algorithms, but despite to be an apparently complex
system, a neural network is relatively simple.
Artificial Neural Networks (ANN) are among the newest signal-processing technologies in the
engineer’s toolbox. The field is highly interdisciplinary, but our approach will restrict the view
to the engineering perspective. In engineering, neural networks serve two important functions:
as pattern classifiers and as nonlinear adaptive filters. We will provide a brief overview of the
theory, learning rules, and applications of the most important neural network models. Definitions
and Style of Computation An Artificial Neural Network is an adaptive, most often nonlinear
system that learns to perform a function (an input/output map) from data. Adaptive means
that the system parameters are changed during operation, normally called the training phase.
After the training phase the Artificial Neural Network parameters are fixed and the system is
deployed to solve the problem at hand (the testing phase ). The Artificial Neural Network is
built with a systematic step-by-step procedure to optimize a performance criterion or to follow
some implicit internal constraint, which is commonly referred to as the learning rule . The input/
output training data are fundamental in neural network technology, because they convey the
necessary information to “discover” the optimal operating point. The nonlinear nature of the
neural network processing elements (PEs) provides the system with lots of flexibility to achieve
practically any desired input/output map, i.e., some Artificial Neural Networks are universal
mappers . There is a style in neural computation that is worth describing.

An input is presented to the neural network and a corresponding desired or target response set
at the output (when this is the case the training is called supervised ). An error is composed from
the difference between the desired response and the system output. This error information is fed
back to the system and adjusts the system parameters in a systematic fashion (the learning rule).
The process is repeated until the performance is acceptable. It is clear from this description that
the performance hinges heavily on the data. If one does not have data that cover a significant
portion of the operating conditions or if they are noisy, then neural network technology is
probably not the right solution. On the other hand, if there is plenty of data and the problem is
poorly understood to derive an approximate model, then neural network technology is a good
choice. This operating procedure should be contrasted with the traditional engineering design,
made of exhaustive subsystem specifications and intercommunication protocols. In artificial
neural networks, the designer chooses the network topology, the performance function, the
learning rule, and the criterion to stop the training phase, but the system automatically adjusts
the parameters. So, it is difficult to bring a priori information into the design, and when the
system does not work properly it is also hard to incrementally refine the solution. But ANN-
based solutions are extremely efficient in terms of development time and resources, and in many
difficult problems artificial neural networks provide performance that is difficult to match with
other technologies. Denker 10 years ago said that “artificial neural networks are the second best

76 LoveLy professionaL university