الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Research work in the area of extracting rules from tranined neural networks has observed much activity recently.Research work in the area of extracting rules from trained
neural networks has observed much activity recently.
However, the degree of complexity of ANN increases
exponentially as a factor of the numbers of input and
hidden nodes. The complexity problem can be improved by
constructing the structure of the network based on
constructive learning
One of the most difficulties for the extraction of
accurate knowledge is that the data being mined can be so
noisy. In these cases neural networks are a feasible
solution, due to their relatively good tolerance to noisy and
generalization ability and the performance of a neural
network is directly related to its parameters and
architecture. But the difficulty is that the classification rules
generated by neural networks are usually hardly
comprehensible to the human users and this is a serious
problem for the user. This is because neural networkcharacterized by real-valued parameters that are difficult to
interpret. This problem can be solved by constructing hyper system from neural network and a new technique of
Evolutionary Algorithms (EAs) called Gene Expression
Programming (GEP).Gene expression programming (GEP) is the most
recent technique of evolutionary algorithm, for data
analysis. GEP uses fixed length, linear strings of
chromosomes to represent computer programs in the form
of expression trees of different shapes and sizes, and
implements a GA to find the best program. Gene
expression programming is one of several linear variations
and liner chromosomes of GP and its aim is to improve the
performance of GP and to offer many options of
implementing GP in the 3rd and the 4th generations of
computer programming languages.In this thesis, we presented destructive neural
network learning technique and the analysis of the
convergence rate of the error in a destructive neural
network with and without threshold in the output layer at
first.In addition, an algorithm has been proposed for rules
extraction based on a trained neural network using Gene
Expression Programming (GEP).We applied our method topublic-domain dataset in order to emphasize that the set of
rules extraction from the proposed method is more accurate
and brief compared with those obtained by the other
models.