Lucks, Marcio B. [UNESP]Nobuo, Oki [UNESP]2014-05-272014-05-272008-09-30CIMSA 2008 - IEEE Conference on Computational Intelligence for Measurement Systems and Applications Proceedings, p. 23-27.http://hdl.handle.net/11449/70591A RBFN implemented with quantized parameters is proposed and the relative or limited approximation property is presented. Simulation results for sinusoidal function approximation with various quantization levels are shown. The results indicate that the network presents good approximation capability even with severe quantization. The parameter quantization decreases the memory size and circuit complexity required to store the network parameters leading to compact mixed-signal circuits proper for low-power applications. ©2008 IEEE.23-27engFunction approximationQuantized parametersRadial basis function networkArtificial intelligenceChlorine compoundsFeedforward neural networksIntelligent controlNetworks (circuits)Polynomial approximationApproximation propertiesCircuit complexityComputational intelligenceInternational conferencesLow-power applicationsMeasurement systemsMemory sizeMixed-signal circuitsNetwork parametersQuantization levelsSimulation resultsSinusoidal functionsRadial basis function networksTrabalho apresentado em evento10.1109/CIMSA.2008.4595826WOS:000259443400006Acesso aberto2-s2.0-52449111383