Enhanced Fuzzy Single Layer Perceptron

Abstract. In this paper, a method of improving the learning time and convergence rate is proposed to exploit the advantages of artificial neural networks and fuzzy theory to neuron structure. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in neural network structure, and recognition of digit image in the vehicle plate image for practical image recognition. As a result of experiments, it does not always guarantee the convergence. However, the network was improved the learning time and has the high convergence rate. The proposed network can be extended to an arbitrary layer. Though a single layer structure is considered, the proposed method has a capability of high speed during the learning process and rapid processing on huge patterns.

• Type: Conference
• Year: 2005
• Where: ISNN
• Authors: Kwang-Baek Kim, Sungshin Kim, Young Hoon Joo, Am Sok Oh
• Keywords: Convergence Rate, ISNN 2005, Learning Time, Neural Network