The general probabilistic fuzzy set for modeling and its application in EMG robots
What is it about?
The probabilistic fuzzy set (PFS) is designed for handling uncertainties with both fuzzy and stochastic nature, so the probabilistic fuzzy logic system (PFLS) has the ability to handle more complex uncertainties in process. In this paper, the general probabilistic fuzzy set is proposed, and the convergence analyses of its secondary probability density function (PDF) are conducted. It discloses the distribution regularity of membership degree in general PFS, which improves the information and interpretability of PFS. Then, according to convergence, a new method to tuning parameters for PFLS is proposed. This method avoids the parameters into local inefficiency, and also reduces the number of learning parameters in PFLS. Last, the new tuning method is applied to the electromyography (EMG) robots modeling problem. The comparison shows that the probabilistic fuzzy logic system based on general PFS (GPFLS) can achieve a simple modeling process, and also, it improves the learning speed compared to PFLS. The work presented will improve the potential application of probabilistic fuzzy logic system.
Why is it important?
In this paper, a new probabilistic fuzzy set named general PFS is proposed by randomly varying the width of Triangular membership function. Its convergence that fuzzy membership degree in this set can approximated by unimodal distribution is derived. This general PFS has better interpretability, and is able to learn more information from original data. A new method for tuning parameters of PFLS is proposed. This method reduces the parameter number and improves the modeling speed of PFLS. Finally, the probabilistic fuzzy logic system based on general PFS is applied to EMG manipulator modeling problem.