A hybrid fuzzy-fractal approach for time series analysis and plant monitoring

Abstract

We describe in this article a new hybrid fuzzy-fractal approach for plant monitoring. We use the concept of the fractal dimension to measure the complexity of a time series of observed data from the plant. We also use fuzzy logic to represent expert knowledge on monitoring the process in the plant. In the hybrid fuzzy-fractal approach, a set of fuzzy if-then rules are used to classify different conditions of the plant. The fractal dimension is used as an input linguistic variable in the fuzzy system to improve the accuracy in the classification. An implementation of the proposed approach is shown to describe in more detail the method.

An Interval Type-3 Fuzzy–Fractal Approach for Plant Monitoring

“…Recently, we have seen the utilization of neural networks, evolutionary computing, and fuzzy systems in this area. In the particular case of fuzzy logic, we can find that most of the works in the literature for monitoring are based on the simplest form of fuzzy logic [1][2][3], which is called type-1, like the works that can be reviewed in [4][5][6][7][8][9][10][11][12]. More recently, type-2 has also been considered in this area, as a way to model uncertainty in a better fashion and achieve better results, as can be verified in [13,14].…”

“…The relevance of performing monitoring for plant processes is now mostly accepted due to the fact that it contributes to enhanced productivity, increased quality, and cost minimization [4,5]. The commonly utilized monitoring methods include pattern recognition methods [6], fuzzy systems [7], knowledge methods [8], neural networks [12], and metaheuristic methods [7].…”

“…where denotes union of all x, u, v values If µ A (3) (x, u, v) = 1, the T3 FS A (3) is simplified to an interval type-3 fuzzy set (IT3 FS) denoted A, postulated by Expression (4).…”

Intelligent control of a stepping motor drive using an adaptive neuro?fuzzy inference system

“…Therefore, the fuzzy logic algorithm is much closer in spirit to human thinking than traditional logical systems [5,19]. The main problem with fuzzy logic controller generation is related to the choice of the regulator parameters [15,21].…”

Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy approach

“…Therefore, the fuzzy logic algorithm is much closer in spirit to human thinking than traditional logical systems [5,15]. The main problem with fuzzy logic controller generation is related to the choice of the regulator parameters [12].…”