LEADER 02873nam 2200493 450 001 9910484868203321 005 20210406130732.0 010 $a3-030-62133-2 024 7 $a10.1007/978-3-030-62133-9 035 $a(CKB)4100000011585997 035 $a(MiAaPQ)EBC6403596 035 $a(DE-He213)978-3-030-62133-9 035 $a(PPN)252508378 035 $a(EXLCZ)994100000011585997 100 $a20210406d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDifferential evolution algorithm with type-2 fuzzy logic for dynamic parameter adaptation with application to intelligent control /$fOscar Castillo, Patricia Ochoa, Jose Soria 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (VII, 61 p. 47 illus., 42 illus. in color.) 225 1 $aSpringerBriefs in applied sciences and technology. Computational intelligence 311 $a3-030-62132-4 330 $aThis book focuses on the fields of fuzzy logic, bio-inspired algorithm, especially the differential evolution algorithm and also considering the fuzzy control area. The main idea is that these two areas together can help solve various control problems and to find better results. In this book, the authors test the proposed method using five benchmark control problems. First, the water tank, temperature, mobile robot, and inverted pendulum controllers are considered. For these 4 problems, experimentation was carried out using a Type-1 fuzzy system and an Interval Type-2 system. The last control problem was the D.C. motor, for which the experiments were performed with Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. When we use fuzzy systems combined with the differential evolution algorithm, we can notice that the results obtained in each of the controllers are better and with increasing uncertainty, the results are even better. For this reason, the authors consider in this book the proposed method using fuzzy systems and the differential evolution algorithm to improve the fuzzy controllers? behavior in complex control problems. 410 0$aSpringerBriefs in applied sciences and technology.$pComputational intelligence. 606 $aFuzzy logic 606 $aEvolution equations 615 0$aFuzzy logic. 615 0$aEvolution equations. 676 $a511.3 700 $aCastillo$b Oscar$0762265 702 $aOchoa$b Patricia 702 $aSoria$b Jose? 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484868203321 996 $aDifferential evolution algorithm with type-2 fuzzy logic for dynamic parameter adaptation with application to intelligent control$92846066 997 $aUNINA