Abstract:
This paper proposes two types of artificial
neural networks to classify the mushroom in order
to compare the performances of two networks;
radial basis function and multilayer perceptron.
Mushrooms are inexpensive and available yearround.
All forms of mushrooms, dried, canned,
frozen or fresh, are healthy. Certainly mushroom
field guides that edible ones and avoid the
poisonous ones, but often mushrooms are hard to
classify. By using mushroom datasets, RBF (radial
basis function) and MLP (multilayer perceptron)
will classify the input features of mushroom into
two classes of edible and poisonous. In this
system, mushrooms datasets from UCI(University
of California) Machine learning repository. Each
mushroom instance consists of seven features that
represent the input layer to the neural network.
RBF and MLP are compared with training time
and accuracy. This system was implemented based
on java programming language.
