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 …

The performance of symbolic inference tasks has long been a challenge to connectionists. In
this paper, we present an extended survey of this area. Existing connectionist inference …

Artificial neural networks (ANNs) have gained extensive popularity in recent years.
Research activities are considerable, and the literature is growing. Yet, there is a large …

Support vector machines (SVMs) have shown superior performance compared to other
machine learning techniques, especially in classification problems. Yet one limitation of …

An important drawback of many artificial neural networks (ANN) is their lack of explanation
capability [Andrews, R., Diederich, J., & Tickle, AB (1996). A survey and critique of …

Artificial neural networks (ANNs) are a powerful and widely used pattern recognition
technique. However, they remain" black boxes" giving no explanation for the decisions they …

Artificial neural networks can be trained to perform excellently in many application areas.
Whilst they can learn from raw data to solve sophisticated recognition and analysis …

In the past, neural networks have been viewed as classification and regression systems
whose internal representations were extremely difficult to interpret. It is now becoming …

This book describes new tools for front end analog designers, starting with global variation-
aware sizing, and extending to novel variation-aware topology design. The tools aid design …

In this paper, we investigate the use of canonical form functions to evolve human-
interpretable expressions for symbolic regression problems. The approach is simple to …