Learning associations of conjuncted fuzzy sets for data prediction

— 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 nonli...

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Hanlin Goh, Joo-Hwee Lim, Chai Quek
• Keywords: Artificial Intelligence, Fuzzy If-then Rules, Fuzzy Neural Network, Fuzzy Sets, IJCNN 2008