This chapter provides an introduction to the field of hybrid neural systems. Hybrid neural
systems are computational systems which are based mainly on artificial neural networks but …

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 …

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 …

Hybrid neural systems are computational systems which are based mainly on artificial
neural networks and allow for symbolic interpretation or interaction with symbolic …

This paper describes techniques for integrating neural networks and symbolic components
into powerful hybrid systems. Neural networks have unique processing characteristics that …

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide
range of applications. They are, however, not considered sufficient for knowledge …

Radial basis neural (RBF) networks provide an excellent solution to many pattern
recognition and classification problems. However, RBF networks are also a local …

Neural networks provide excellent solutions for pattern recognition and classification
problems. Unfortunately, in the case of distributed neural networks such as the multilayer …

Este trabalho descreve experimentos realizados com Redes Neurais Artificiais e algoritmos
de aprendizado simbólico. Também são investigados dois algoritmos de extração de …

Artificial neural networks (ANNs) and support vector machines have successfully improved
the quality of predicting share movements in relation to statistically based counterparts …