A Simple Fuzzy Neural Network

Our simple fuzzy neural network first thins the set of exemplar input feature vectors and then centers a Gaussian function on each remaining one and saves its associated output label (target). Next, any unknown feature vector to be classified is put through each Gaussian to get the fuzzy truth that it belongs to that center. The fuzzy truths for all Gaussian centers are then maximized and the label of the winner is the class of the input feature vector. We use the knowledge in the exemplar-label pairs directly with no training, no weights, no local minima, no epochs, no defuzzification, no overtraining, and no experience needed to use it. It sets up automatically and then classifies all input feature vectors from the same population as the exemplar feature vectors. We compare our results on well known data with those of several other fuzzy neural networks, which themselves compared favorably to other neural networks.

• Type: Conference
• Year: 2007
• Where: CAINE
• Authors: Carl G. Looney, Sergiu Dascalu
• Keywords: CAINE 2007, Computer Science, Feature Vectors, Fuzzy Neural Networks, Input Feature Vectors