Name
Abstract | ||
|---|---|---|
Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1007/BF00125491 | Journal of Intelligent and Robotic Systems |
Keywords | Field | DocType |
Neural networks,adaptive control,self-organising networks | Speech processing,ENCODE,Computer science,Image processing,Artificial intelligence,Adaptive control,Artificial neural network,Classifier (linguistics),State space,Robotics,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 2 | 0921-0296 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Warwick | 1 | 129 | 21.37 |
| Nigel Ball | 2 | 3 | 0.83 |