Topology constraint free fuzzy gated neural networks for pattern recognition

In this paper, a novel topology constraint free neural network architecture using a generalized fuzzy gated neuron model is presented for pattern recognition task. The main feature is that the network does not require weight adaptation at its input and the weights are initialized directly from the training pattern set. The elimination of the need for iterative weight adaptation schemes facilitates quick network set up times which make the fuzzy gated neural networks very attractive. The performance of the proposed network is found to be functionally equivalent to spatio-temporal feature maps under a mild technical condition. The classification performance of fuzzy gated neural network is demonstrated on a 12-class synthetic three-dimensional (3-D) object data set, real-world eight-class texture data set, and real-world 12-class 3-D object data set. The performance results are compared with the classification accuracies obtained from spatiotemporal feature map, adaptive subspace self-organizing map, multilayer feedforward neural networks, radial basis function neural networks, and linear discriminant analysis. Despite the network's ability to accurately classify seen data and adequately generalize validation data, its performance is found to be sensitive to noise perturbations due to fine fragmentation of the feature space. This paper also provides partial solutions to the above robustness issue by proposing certain improvements to various modules of the proposed fuzzy gated neural network.