New axioms for the contrast model of similarity
Journal of Mathematical Psychology
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Unified theories of cognition
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Neurocomputing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
An introduction to natural computation
An introduction to natural computation
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Neural Assemblies, an Alternative Approach to Artificial Intelligence
Neural Assemblies, an Alternative Approach to Artificial Intelligence
TEST: a model-driven application shell
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Neural associative memory with optimal bayesian learning
Neural Computation
| Hi-index | 0.01 |
We describe a neuronal model for diagnostic problem-solving. This model which is inspired by cell assemblies gives some hints on how diagnostic problem-solving might actually be performed by the human brain. The diagnostic process is described by a deduction system that performs an abductive inference. The abductive inference itself is described by the verbal category theory. A mapping of a diagnostic problem into a diagnostic system represented by an associative memory with feedback connections is presented. The associative memory with feedback connections offers a self-contained architecture for the administration and representation of manifestations and disorders. This can be implemented efficiently on a serial computer, requiring low memory space and low computational costs. Because of these advantages, this model was chosen for the implementation of a real embedded diagnostic system for a wire bonder machine. The knowledge base of this system is composed of 350 rules, which are stored in 11 modules. These modules model the error behaviour of the microcontroller based units of the machine and are arranged in a taxonomy which corresponds to the hierarchical chains that describe the relationship between disorders and manifestations.