3 research outputs found
Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective
Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curveâfitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fineâtuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fineâtuning, fuzzy ruleâbased learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programmingâbased EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods
A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations Um sistema neuro-difuso para auxiliar no diagnóstico de eventos epilépticos e eventos não epilépticos utilizando diferentes operaçÔes aritméticas difusas
OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB). RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%); the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.<br>OBJETIVO: Investigar diferentes operaçÔes aritmĂ©ticas difusas para auxĂliar no diagnĂłstico de eventos epilĂ©pticos e eventos nĂŁo-epilĂ©pticos. MĂTODO: Um sistema neuro-difuso foi desenvolvido utilizando a arquitetura NEFCLASS (NEuro Fuzzy CLASSIfication) e uma rede neural artificial com o algoritmo de aprendizagem backpropagation (RNAB). RESULTADOS: A amostra estudada foi de 244 pacientes com maior freqĂŒĂȘncia no sexo feminino. O nĂșmero de decisĂ”es corretas na fase de teste, obtidas atravĂ©s do NEFCLASS e RNAB foi de 83,60% e 90,16%, respectivamente. O melhor resultado de sensibilidade foi obtido com o NEFCLASS (84,90%); o melhor resultado de especificidade foi obtido com a RNAB (95,65%). CONCLUSĂO: O sistema neuro-difuso proposto combinou a capacidade das redes neurais artificiais na classificação de padrĂ”es juntamente com a abordagem qualitativa da logica difusa, levando a maior taxa de acertos do sistema