A Fuzzy Inference System (FIS) typically implements a function f : R → T, where the domain set R denotes the totally-ordered set of real numbers, whereas the range set T may be either T = R (i.e. FIS regressor) or T may be a set of labels (i.e. FIS classifier), etc. This work considers the complete lattice (F,1) of Type-1 Intervals’ Numbers, or INs for short, where an IN F can be interpreted as...

service availability is important for any organization. this has become more important with the increase of dos attacks. it is therefore essential to assess the threat on service availability. we have proposed a new model for threat assessment on service availability with a data fusion approach. we have selected three more important criteria for evaluating the threat on service availability and...

Multivariable liquid level control is essential in process industries to ensure quality of the product and safety of the equipment. However, the significant problems of the control system include excessive time consumption and percentage overshoot, which result from ineffective performance of the tuning methods of the PID controllers used for the system. In this paper, fuzzy logic was used to t...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

Sensor failure detection and identification has been considered as an important issue, particularly when the measurements from sensors are used in the feedback loop of a control law. Kalman filters and Luenberger observers have been widely used to generate signal redundancy by means of state estimation. Dedicated observer scheme and generalized observer scheme are the older methods available fo...

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation...

Slope stability analysis is an enduring research topic in the engineering and academic sectors. Accurate prediction of the factor of safety (FOS) of slopes, their stability, and their performance is not an easy task. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was utilized to build an estimation model for the prediction of FOS. Three ANFIS models were implemented including g...

organizational performance is a complex issue given that performance is a multifaceted phenomenon whose components may have distinct managerial priorities and may even be mutually inconsistent. recently, the balanced scorecard approach (bsc), as an effective multi-criteria evaluation concept received much attention in organizational performance measurement. although the bsc conceptual framework...

The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. Those rules are not explicit for the expert. This paper proposes a new method to generate Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps. The first step consists in using the subtractive clustering ...