An Analytical Approach to study Annealing Induced Interdiffusion of In and Ga for Truncated Pyramidal InAs/GaAs Quantum Dots

摘要： The brain–computer interface (BCI) identi?es brain patterns to translate thoughts into action. The identi?cation relies on the performance of the classi?er. In this paper, identi?cation and monitoring of electroencephalogram-based BCI for motor imagery (MI) task is proposed by an ef?cient adaptive neuro-fuzzy classi?er (NFC). The Jaya optimization algorithm is integrated with adaptive neuro-fuzzy inference systems to enhance classi?cation accuracy. The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. In this paper, Jaya-based k-means is applied to divide the feature set into two mutually exclusive clusters and ?re the fuzzy rule. The performance of the proposed classi?er, Jaya-based NFC using SSCG as training algorithm and is powered by LH (JayaNFCSSCGLH), is compared with four different NFCs for classifying two class MI-based tasks. We observed a shortening of computation time per iteration by 57.78% in the case of SSCG as compared with the SCG technique of training. LH-based feature selecting capability of the proposed classi?er not only reduces computation time but also improves the accuracy by discarding irrelevant features. Lesser computation time with fast convergence and high accuracy among considered NFCs make it a suitable choice for the real-time application. Supremacy of JayaNFCSSCGLH among the considered classi?er is validated through Friedman test. Classi?cation result is used to control switching of light emitting diode, turning thoughts into action.