This work substantiates novel perspectives and tools for analysis and design of Fuzzy Inference Systems (FIS). It is shown rigorously that the cardinality of the set F of fuzzy numbers equals א1, hence a FIS can implement “in principle” א2 functions, where א2 = 2א1>א1 and א1 is the cardinality of the set R of real numbers; furthermore a FIS is endowed with a capacity for local generalization. A...

Despite of the popularity of the fuzzy Failure Mode and Effects Analysis (FMEA) methodology, there are several limitations in combining the Fuzzy Inference System (FIS) and the Risk Priority Number (RPN) model. Two main limitations are: (1) it is difficult and impractical to form a complete fuzzy rule base when the number of required rules is large; and (2) fulfillment of the monotonicity prope...

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle (1982) and Cheng, Chen, and Wei (2010). After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), ...

in this paper, an adaptive neuro fuzzy inference system (anfis) based control is proposed for the tracking of a micro-electro mechanical systems (mems) gyroscope sensor. the anfis is used to train parameters of the controller for tracking a desired trajectory. numerical simulations for a mems gyroscope are looked into to check the effectiveness of the anfis control scheme. it proves that the sy...

This paper presents a Dynamic Fuzzy Q-Learning (DFQL) method that is capable of tuning the Fuzzy Inference Systems (FIS) online. On-line self-organizing learning is developed so that structure and parameters identification are accomplished automatically and simultaneously. Selforganizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuou...

This paper describes the optimization of an integration block within the proposed navigation control system for a mobile robot. The control blocks that the integrator will combine are two Fuzzy Inference Systems (FIS) in charge of tracking and reaction control, respectively. The integrator block is called a Weighted Fuzzy Inference System (WFIS) and assigns weigh...

This report takes advantage of Neural Networks (NN) and Fuzzy Inference Systems (FIS) in order to design a system suited to predict time series. We choose the solution of the Mackey{Glass time delay diierential equation in the chaotic domain as a sample problem. Fuzzy rules are generated from the sample data. The system performance is improved by means of Evolution Strategy (ES). The rules of t...

This work presents an approximation to the modeling and identification tasks using Fuzzy Inference Systems (FIS) applied to the Benchmark Problem: pH neutralization control in industrial processes. The statement of this problem is taken from a paper that postulates the waste water pH neutralization process as a benchmark approach. The proposed model uses the Takagi-Sugeno FIS type, aiming at fu...

Nowadays, several techniques such as; Fuzzy Inference System (FIS) and Neural Network (NN) are employed for developing of the predictive models to estimate parameters of water quality. The main objective of this study is to compare between the predictive ability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model and Artificial Neural Network (ANN) model to estimate the Biochemical Oxyge...

cementation factor is a critical parameter, which affects water saturation calculation. in carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. hence developing a reliable mathematical strategy to determine these properties accurately is of crucial importance. to this end, genetic algorithm pattern search is emp...