COMBINING NEURAL NETWORKS AND FUZZY LOGIC FOR APPLICATIONS IN CHARACTER RECOGNITION

AUTOR(ES)

Anne Magály de Paula Canuto

DATA DE PUBLICAÇÃO

2001

RESUMO

This thesis investigates the benefits of combining neural networks and fuzzy logic into neuro-fuzzy systems, especially for applications in character recognition tasks. The research reported in this thesis is divided into two parts. In the first part, two main neurofuzzy systems are described and investigated - the fuzzy MLP and RePART models. The former is the result of combining fuzzy logic and a multi-layer perceptron network. The latter is the result of combining fuzzy logic and an ARTMAP network. In the description of both neuro-fuzzy systems, some proposals to improve the performance of their corresponding models are also described. An analysis comparing the model enhancements proposed in this thesis with the corresponding original models in a character recognition task is also presented. Very promising results have been obtained in the sense that both an improvement in the recognition rate and a reduction in the complexity of the models are achieved using the models proposed in this thesis. In the second part of this research, the two aforementioned neuro-fuzzy models together with a non-fuzzy model (radial RAM), are used as components of a multi-neural system in an experimental study. In this part, the focus is on combination methods to be used in order to improve the performance of the multi-neural system. A variety of combination methods have been investigated - fuzzy, neural neuro-fuzzy and conventional combiners - and it has been demonstrated that the neuro-fuzzy combiner delivers the best performance of all combination methods analysed. Once more, this result confirms the importance of combining these two technologies (neural networks and fuzzy logic) in character recognition tasks.

ASSUNTO(S)

redes neurais artificiais neuro-fuzzy systems sistemas multi-classificadores lógica fuzzy neural networks sistemas inteligentes engenharia eletrica redes neuro-fuzzy inteligência artificiall

Documentos Relacionados

• TRAFFIC CONTROL THROUGH FUZZY LOGIC AND NEURAL NETWORKS
• ARTIFICIAL NEURAL NETWORKS, FUZZY LOGIC AND NEURO-FUZZY SYSTEM IN THE ROLE OF SHORT TERM LOAD FORECAST
• Pattern recognition, chaos, and multiplicity in neural networks of excitable systems.
• Ribosome binding site recognition using neural networks
• Fuzzy logic for structural system control