In order to solve the blind source deconvolution, a convolved mixing process in the time domain is often transformed into an instantaneous mixing model in the frequency domain. However, the model is only an approximated one and thus does not work effectively under highly reverberant environments. By dividing the impulse response properly, Servière has precisely transformed the time-domain convolved mixture to a frequency-domain convolved mixture and has proposed a new FDICA approach available under high reverberation. In the approach, however, the permuation and scaling problems are unresolved as well as the distortion due to whitening. In the present paper, an improved approach without such problems is proposed and is confirmed to be valid in a highly reverberant real environment
The existence of stochastic Lyapunov functions assures that the systems are stable in probability. However, general scheme of obtaining stochastic Lyapunov functions has not been introduced yet. This paper extends the authors’ previous method obtaining Lyapunov functions for deterministic cases to stochastic cases. In the method, difference-approximation scheme[6,10] and quantization of Markov processes[1,14] are used to approximate Lyapunov equations by linear quantum-like equations. We propose a method of obtaining approximate stochastic Lyapunov functions for nonlinear stochastic systems by superimposing the eigenfunctions of the quantum-like equations.
In this paper, we propose a control method for a rotary crane system using Direct Gradient Descent Control (DGDC) optimized by a genetic algorithm (GA). The rotary crane is known to be a class of nonholonomic system and the DGDC method is useful for the nonholonomic system control.
However, the control performance of the DGDC strongly depends on the parameters of controller and the value of initial control inputs. Also, for usage of the DGDC, it is required to re-design iteratively the control parameters or the control inputs. We applied GA optimization to determine the sets of control parameters of the DGDC for the crane system. The parameters of the DGDC optimized by GA are not required to be re-designed. Simulation results show that the proposed method has higher control performance and has good robustness with noise and fluctuation of the initial states.