Neural network learning and expert systems
Neural network learning and expert systems
Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
Integrating rules and connectionism for robust commonsense reasoning
Integrating rules and connectionism for robust commonsense reasoning
SETHEO: a high-performance theorem prover
Journal of Automated Reasoning
Knowledge-based artificial neural networks
Artificial Intelligence
Hybrid Neural Network and Expert Systems
Hybrid Neural Network and Expert Systems
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
A Recency Inference Engine for Connectionist Knowledge Bases
Applied Intelligence
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In this paper, we present a method for improving the performance of classical symbolic rules. This is achieved by introducing a type of hybrid rules, called neurules, which integrate neurocomputing into the symbolic framework of production rules. Neurules are produced by converting existing symbolic rules. Each neurule is considered as an adaline unit, where weights are considered as significance factors. Each significance factor represents the significance of the associated condition in drawing the conclusion. A rule is fired when the corresponding adaline output becomes active. This significantly reduces the size of the rule base and, due to a number of heuristics used in the inference process, increases inference efficiency.