data-driven state estimation of carbon nanotube field effect transistor with smart rbf network

Since 1993, devices based on cnts have applications ranging from nanoelectronics to optoelectronics. the challenging issue in designing these devices is that the nonequilibrium green's function (negf) method has to be employed to solve the schrödinger and poisson equations, which is complex and time consuming. in the present study, a novel smart and optimal algorithm is presented for fast and accurate modeling of cnt fieldeffect transistors (cntfets) based on an artificial neural network. a new and efficient way is presented for incrementally constructing radial basis function (rbf) networks with optimized neuron radii to obtain the estimator network. an incremental extreme learning machine (i-elm) algorithm is used to train the rbf network. to ensure the optimal radii for incremental neurons, this algorithm utilizes a modified version of an optimization algorithm known as the nelder-mead simplex algorithm. results confirm that the proposed approach reduces the network size for faster error convergence while preserving the estimation accuracy.