Weights Of Fuzzy Rules Research Articles

Robust Design of an Ultra-Thin Centrifugal Fan on Fuzzy-Based Taguchi Method

The objective of this paper is to apply the Taguchi method with fuzzy inference for optimization the design of an ultrathin centrifugal fan with multiple performance characteristics. The weight of fuzzy rule controlling the system can be adjusted in this study. Taguchi’s concepts of orthogonal array, signal to noise (S/N) ratio, ANOVA, and weight of fuzzy rule were used to study the multiple-objectives in the ultrathin centrifugal fan design. The results showed that: (1) the inlet dimensions have a significant influence on the volume flow rate, whereas outlet dimensions, inlet dimensions and blade angle have a significant effect on the static pressure; (2) appropriate adjustments the weight of fuzzy rule can be obtained a better design parameters for the objective.

An error-based on-line rule weight adjustment method for fuzzy PID controllers

In this study, a new method is proposed for the adjustment of the fuzzy rule weights of the fuzzy PID controllers in an on-line manner. For this purpose, the transient phase of the unit response of the closed loop system is taken into consideration. The transient phase of the response is assumed to be divided into certain regions which are assigned in accordance with the number of membership functions defined for the error input of the fuzzy logic controller. Then, the relative importance or influence of the fired fuzzy rules of the fuzzy logic controller are determined for each region and the meta-rules are derived for the adjustment of corresponding fuzzy rule weight values to obtain an ‘efficient’ and ‘appropriate’ control signal that will achieve a “desired” system response. Since the value of system error varies during the transient system response and it is on hand for each region and sampling time, the weight tuning is accomplished using this error value. For this purpose, two simple functions based on the absolute value of the normalized system error are directly used for the assignment of the rule weights by an adequate arrangement in accordance with the meta-rules derived. By these assignments the error value is charged as the tuning variable of the rule weights and thus an on-line self tuning rule weight assignment is accomplished. The effectiveness of the proposed self tuning method is demonstrated on linear and non-linear systems by simulations and a real time application is done on Process Control Simulator.

Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis

In fuzzy rule-based classification systems, rule weight has often been used to improve the classification accuracy. In past research, a number of heuristic methods for rule weight specification have been proposed. In this paper, a method of fuzzy rule weight specification using Receiver Operating Characteristic (ROC) analysis is proposed. In order to specify the weight of a fuzzy rule, using 2-class ROC analysis, the threshold that the rule achieves its maximum accuracy is found. This threshold is used as the weight of the rule. The proposed method is compared with existing ones through computer simulations on some well-known classification problems with continuous attributes. Simulation results show that the proposed method performs better than existing methods of fuzzy rule weight specification.

A Neuro-Fuzzy Approach to Gear System Monitoring

The detection of the onset of damage in gear systems is of great importance to industry. In this paper, a new neuro-fuzzy diagnostic system is developed, whereby the strengths of three robust signal processing techniques are integrated. The adopted techniques are: the continuous wavelet transform (amplitude) and beta kurtosis based on the overall residual signal, and the phase modulation by employing the signal average. Three reference functions are proposed as post-processing techniques to enhance the feature characteristics in a way that increases the accuracy of fault detection. Monitoring indexes are derived to facilitate the automatic diagnoses. A constrained-gradient-reliability algorithm is developed to train the fuzzy membership function parameters and rule weights, while the required fuzzy completeness is retained. The system output is set to different monitoring levels by using an optimization procedure to facilitate the decision-making process. The test results demonstrate that the novel neuro-fuzzy system, because of its adaptability and robustness, significantly improves the diagnostic accuracy. It outperforms other related classifiers, such as those based on fuzzy logic and neuro-fuzzy schemes, which adopt different types of rule weights and employ different training algorithms.