Real-Valued GCS Classifier System

Real-Valued GCS Classifier System

Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the cont...

Reinforcement Learning by an Accuracy-Based Fuzzy Classifier System with Real-valued Output

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform on-line reinforcement learning by means of Michigan-style fuzzy classifier systems, this issue becomes even more difficult. Indeed, rule generalization (description of state-action relationships ...

A Fully Complex-valued Fast Learning Classifier (FC-FLC) for real-valued classification problems

This paper presents a Fully Complex-valued Fast Learning Classifier (FC-FLC) to solve real-valued classification problems. FC-FLC is a single hidden layer network with a nonlinear input and hidden layer, and a linear output layer. The neurons at the input layer of the FC-FLC employ the circular transformation to convert the real-valued input features to the Complex domain. At the hidden layer, ...

A fully complex-valued radial basis function classifier for real-valued classification problems

In this paper, we investigate the decision making ability of a fully complex-valued radial basis function (FC-RBF) network in solving real-valued classification problems. The FC-RBF classifier is a single hidden layer fully complex-valued neural network with a nonlinear input layer, a nonlinear hidden layer, and a linear output layer. The neurons in the input layer of the classifier employ the ...

Classifier System Mapping of Real Vectors