This paper discusses a method for im­ plementing a probabilistic inference system based on an extended relational data model. This model provides a unified approach for a variety of applications such as dynamic pro­ gramming, solving sparse linear equations, and constraint propagation. In this frame­ work, the probability model is represented as a generalized relational database. Subse­ quent p...

In this work, three topics which are important for the further development of fuzzy systems are chosen to be investigated. First, the mathematical aspects of fuzzy relational equations (FREs) are explored. Solving FREs is one of the most important problems in fuzzy systems. In order to identify the algebraic information of the fuzzy space, two new tools, called fuzzy multiplicative inversion an...

The aim of this paper is to present a hybrid group decision model for evaluating flexible manufacturing systems(FMSs), in which the information about attribute weights is completely unknown, and the attribute values take the form of triangular fuzzy numbers. In this proposed methodology, the voting method is adopted to calculate the attribute weights by aggregating the decision-makers’ attitude...

The adaptive fuzzy and fuzzy neural models are being widely used for identification of dynamic systems. This paper describes different fuzzy logic and neural fuzzy models. The robustness of models has further been checked by Simulink implementation of the models with application to the problem of system identification. The approach is to identify the system by minimizing the cost function using...

This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local ...

The multiple reconfiguration and the complexity of the modern production system lead to design intelligent monitoring aid systems. Accordingly, the use of neuro-fuzzy technics seems very promising. In this paper, we propose a new monitoring aid system composed by a dynamic neural network detection tool and a neuro-fuzzy diagnosis tool. Learning capabilities due to the neural structure permit us...

With the number of vehicle increasing, urban traffic congestion became more serious. This paper focused on the architecture layer of the proposed dynamic hybrid fuzzy logic control system, which divided into three main parts: The proposed algorithm (Dynamic Webster with dynamic Cycle Time), Accident Detection System using fuzzy logic theory and Action System depending on Detection System. It co...

The Bandler–Kohout Subproduct (BKS) inference mechanism is one of the two established fuzzy relational inference (FRI) mechanisms, the other being Zadeh’s Compositional Rule of Inference (CRI). Both these FRIs are known to possess many desirable properties. It can be seen that many of these desirable properties are due to the rich underlying structure, viz., the residuated algebra, from which t...

Qualitative modeling of technical processes may be accomplished by dynamic fuzzy systems. A new inference method with interpolating rules is proposed as an essential basis for the analysis of this class of systems. Using this approach, the system output is dependent on both an interpolating rule derived from the fuzzy input and the fuzzy input itself. A simple example shows the typical behavior...

A new systematic algorithm to build adaptive linguistic fuzzy models directly from input-output data is presented in this paper. Based on clustering and projection in the input and output spaces, significant inputs are selected, the number of clusters is determined, rules are generated automatically, and a linguistic fuzzy model is constructed. Then, using a simplified fuzzy reasoning mechanism...