Parameter-induced aperiodic stochastic resonance in the presence of multiplicative noise and additive noise

Parameter-induced aperiodic stochastic resonance (SR) in the presence of multiplicative and additive noise is investigated in detail. Using theoretical and numerical analysis, we evaluate the dynamical probability density, by which the bit error rate is defined as a quantity to optimize in the aperiodic SR problem via tuning the system parameters. Tuning the system parameters includes two cases: one is parameter-optimized and the other is parameter-induced SR. To distinguish them, we use the conventional method in which SR is realized by varying the noise intensity. Essentially, parameter-induced SR is based on the cooperative effect between the stochastic-subjected dynamical system and the input signal by changing the intrinsic characteristic of the nonlinear system via tuning the system parameters, rather than by adding noise. The theory is tested against numerical simulations