In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the parameters. This kind of aleatory uncertainty is irreduc...

This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies...

Metal matrix composites have been increasingly used as materials for components in automotive and aerospace industries because of their improved properties compared with non-reinforced alloys. During machining the selection of appropriate machining parameters to produce job for desired surface roughness is of great concern considering the economy of manufacturing process. In this study, a surfa...

The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing fuzzy time series (FTS) algorithm. MWTS has overcome certain limitations of FTS, such as repetition logic relationships and weight considerations relationships. main challenge the MWFTS absence standardized rules determining partition intervals. This study compares model to methods Genetic Algor...

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a panel exhibits multiple peaks under partial shading condition (PSC) when all modules do not receive the same irradiation. Although conventional PSO has shown perform well uniform insolation, it is often unable fin...

Image colour classification and Image segmentation using comprehensive learning particle swarm optimization (CLPSO) technique was developed by Parag Puranik, Dr. P.R. Baja, Prof. P.M. Palsodkar [1], the aim was to produce a fuzzy system for colour classification and image segmentation with least number of rules and minimum error rate. In this paper we propose exponential particle swarm optimiza...