In this paper, real-time implementation of multirobot convoying behavior utilizing neuro-fuzzy control is presented. With only nine rules, the follower convoys the leader closely and smoothly by varying its speed and changing its direction. When the leader stops, the follower stops very close to the specified safe distance (within an inch). The follower backs up quickly when the leader moves ba...

A business organization’s objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence technique...

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This paper proposes a new neuro-fuzzy method to model the dynamic behavior of com'plex system.s based on real experimental data. First, we investigate the firing strength of rules by a fuzzy C-means clustering method. Then, we retrieve the membership functions of input variables by a neuro-fuzzy network. Finally, we identify the parameters of linear local models by recursive least squares. I n ...

An adaptive method to construct compact fuzzy systems for solving pattern classiication problems is presented. The method consists of two phases: a rule identiication phase and a rule selection phase. The rule identiication phase generates fuzzy rules from numerical data through a simple fuzzy grid method, then tunes the resulting fuzzy rules by training a neuro-fuzzy network used to model the ...

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...

Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher va...

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