In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions.

This paper discusses the application of a fuzzy-ARTMAP neural network to digital communications channel equalization. This approach provides new solutions for solving the problems, such as complexity and long training, which found when implementing the previously developed neural-basis equalizers. The proposed fuzzy-ARTMAP equalizer is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capability of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of the proposed equalizer is compared with other neural net basis equalizers, specifically MLP and RBF equalizers.

A new learning method is proposed to enhance the performances of the fuzzy ARTMAP neural network in the noisy environment. It combines the average learning and slow learning for the weight vectors in the fuzzy ARTMAP. It effectively reduces a category proliferation problem and enhances recognition performance for noisy input patterns.

An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.

This paper presents a new learning method to improve noise tolerance in Fuzzy ART. The two weight vectors: the top-down weight vector and the bottom-up weight vector are differently updated by a weighted sum and a fuzzy AND operation. This method effectively resolves the category proliferation problem without increasing the training epochs in noisy environments.