The choice of transfer functions may strongly influence complexity and performance of
neural networks. Although sigmoidal transfer functions are the most common there is no a …

A new methodology of extraction, optimization, and application of sets of logical rules is
described. Neural networks are used for initial rule extraction, local or global minimization …

Several methods were proposed to reduce the number of instances (vectors) in the learning
set. Some of them extract only bad vectors while others try to remove as many instances as …

In this study, we design polynomial-based radial basis function neural networks (P-RBF
NNs) based on a fuzzy inference mechanism. The essential design parameters (including …

In many applications, black-box prediction is not satisfactory, and understanding the data is
of critical importance. Typically, approaches useful for understanding of data involve logical …

Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast
learning of neural networks. Families of parameterized transfer functions provide flexible …

Probability that a crisp logical rule applied to imprecise input data is true may be computed
using fuzzy membership function (MF). All reasonable assumptions about input uncertainty …

In this paper a feature selection algorithm CSSFFS (Constrained search sequential floating
forward search) based on SVM is proposed for detecting breast cancer. It is a greedy …

E-mail: duch@ phys. uni. torun. pl Abstract. One of the biggest challenges of science today is
to outline connections between the subjective world of human experience, as studied by …

Although computational intelligence (CI) covers a vast variety of different methods it still
lacks an integrative theory. Several proposals for CI foundations are discussed: computing …