Fuzzy control is one of the most important parts of fuzzy theory for which several approaches exist. Mamdani uses $alpha$-cuts and builds the union of the membership functions which is called the aggregated consequence function. The resulting function is the starting point of the defuzzification process. In this article, we define a more natural way to calculate the aggregated consequence funct...

Mamdani Fuzzy Model is an important technique in Computational Intelligence (CI) study. This paper presents an implementation of a supervised learning method based on membership function training in the context of Mamdani fuzzy models. Specifically, auto zoom function of a digital camera is modelled using Mamdani technique. The performance of control method is verified through a series of simul...

An algorithm is presented that uses evolutionary programming to construct fuzzy membership functions that are used to extract Zadeh-Mamdani fuzzy rules from a constructive neural network. The algorithm has potential applications in fields such as data mining and knowledge-based decision support systems. Evaluation of the algorithm over two well known benchmark data sets shows that while the res...

In this study, we present a hybrid approach of Ant Colony Optimization algorithm (ACO) with fuzzy logic and clustering methods to solve multiobjective path planning problems in the case swarm Unmanned Surface Vehicles (USVs). This study aims further explore performance ACO by integrating order cope multiple contradicting objectives generate quality solutions in-parallel identifying mission area...

Load sensor is developed using mamdani fuzzy inference system and sugeno fuzzy inference system. It is two input and one output sensor. Both mamdani-type fuzzy inference system and sugeno-type fuzzy inference system are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between these two fuzzy inference system and their simulated results are compared. Index Ter...

This paper presents an adaptation of the COR methodology to derive the rule base in TSK-type linguistic fuzzy rule-based systems. In particular, the work adapts an existing local search algorithm for Mamdani rules which was shown to find the best solutions, whilst reducing the number of evaluations in the learning process.

The paper is focusing on Human Machine-Interface implementation in designing crane control based on fuzzy logic algorithm. The Human Machine-Interface application was created for visualization, monitoring and managing the transportation process realized by the crane. Control systems based on fuzzy controllers with Mamdani and Sugeno inference systems were elaborated and built using prototyping ...

The tradeoff between accuracy and interpretability in fuzzy modeling has shifted into focus in last few years. This paper aims at improving accuracy of linguistic models while maintaining a good interpretability. A new gradient-based method, extended version of Jager approach, is proposed for the optimization of transparent Mamdani systems. The advantage of Mamdani systems if compared to 0 orde...

In this paper, we use a method based on a multi-objective genetic algorithm, namely the Pareto Archived Evolutionary Strategy (PAES), to generate a set of Mamdani fuzzy systems from numerical data. PAES determines an approximation of the optimal Pareto front by concurrently maximizing the accuracy and minimizing the complexity. Unlike other approaches, we measure the complexity as sum of the va...

This paper evaluates the security of wireless communication network based on the fuzzy logic in Mat lab. A new algorithm is proposed and evaluated which is the hybrid algorithm. We highlight the valuable assets in designing of wireless network communication system based on network simulator (NS2), which is crucial to protect security of the systems. Block cipher algorithms are evaluated by usin...