Leveraging stochastic filtering approach in fractional order Colpitts oscillator circuit

Chaos detection in noisy framework is a crucial issue that is significant in several engineering domains. In recent state-of-the-art work, authors proposed different methods for chaos detection; however, they lack reliability, flexibility, and have a greater computational burden. To get rid of the aforementioned limitations, this paper presents the Bayesian filtering-based bifurcation analysis of the Colpitts oscillator to show regular and irregular (chaotic) oscillations. Initially, we formulate fractional-order derivative (FOD) based stochastic differential equations (SDEs) using Kirchhoff’s law by introducing Gaussian noise to circuit elements, then we predicted the chaos using FOC-based adaptive iterated extended Kalman filter (AIEKF) method and compared with the FOC-based extended Kalman filter (EKF) method, FOC-based wavelet transform (WT) method. We also compare the estimated output with PSPICE simulated values and illustrate the efficacy of the proposed approach with respect to the FOC-based conventional method.