A general framework for fuzzy morphological associative memories
Fuzzy Sets and Systems
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
Hybrid intelligent vision-based car-like vehicle backing systems design
Expert Systems with Applications: An International Journal
Permutation-based finite implicative fuzzy associative memories
Information Sciences: an International Journal
Intelligent transportation systems-Enabling technologies
Mathematical and Computer Modelling: An International Journal
Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on the slopes of fuzzy sets
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
Fuzzy control systems and neural-network control systems for backing up a simulated truck, and truck-and-trailer, to a loading dock in a parking lot are presented. The supervised backpropagation learning algorithm trained the neural network systems. The robustness of the neural systems was tested by removing random subsets of training data in learning sequences. The neural systems performed well but required extensive computation for training. The fuzzy systems performed well until over 50% of their fuzzy-associative-memory (FAM) rules were removed. They also performed well when the key FAM equilibration rule was replaced with destructive, or `sabotage', rules. Unsupervised differential competitive learning (DCL) and product-space clustering adaptively generated FAM rules from training data. The original fuzzy control systems and neural control systems generated trajectory data. The DCL system rapidly recovered the underlying FAM rules. Product-space clustering converted the neural truck systems into structured sets of FAM rules that approximated the neural system's behavior