Abstract:
Fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of un...Show MoreMetadata
Abstract:
Fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsupervised and supervised learning. The network first quantizes input and output feature maps using fuzzy sets. They are subsequently conjuncted to form antecedents and consequences, and associated to form fuzzy if-then rules. These associations are learnt through a learning process consisting of three consecutive phases. First, an unsupervised phase initializes based on information density the fuzzy membership functions that partition each feature map. Next, a supervised Hebbian learning phase encodes synaptic weights of the input-output associations. Finally, a supervised error reduction phase fine-tunes the fine-tunes the network and discovers the varying influence of an input dimension across output feature space. FASCOM was benchmarked against other prominent architectures using data taken from three nonlinear data estimation tasks and a real-world road traffic density prediction problem. The promising results compiled show significant improvements over the state-of-the-art for all four data prediction tasks.
Published in: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
Date of Conference: 01-08 June 2008
Date Added to IEEE Xplore: 26 September 2008
ISBN Information: