Evolving a neural network using dyadic connections

—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network controllers for complex systems while minimising the design effort. Using a robot task as a case study, we have shown that using the feedback from the robot itself, the system can learn from experience, or example provided by an expert. We present a system where the processing of the feedback is integrated entirely in the growing of a spiking neural network system. The feedback is extracted from a measurement of a reward interpretation system provided by the designer, which takes into consideration the robot actions without the need for external explicit inputs. Starting with a small basic neural network, new connections are created. The connections are separated into artificial dendrites, which are mainly used for classification issues, and artificial axons, which are responsible for selecting appropriate...

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Andreas Huemer, Mario A. Góngora, David A. Elizondo
• Keywords: Artificial Intelligence, IJCNN 2008, Neural Network, Neural Network Controllers, Robot