Cooperative Robot Structures Modeled After Whale Behavior and Social Structure

Reşceanu, Ionuţ; Resceanu, Cristina Floriana; Bizdoaca, Nicu George

doi:10.15837/ijccc.2012.5.1354

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…Recent research in the field of robot control is conducted in new directions based on bioinspired approach that proved their efficiency on other fields of robotics, like the simplified fuzzy controller using neural network based on genetic algorithm learning for mobile robots [21], the communications and social structure of whale pods applied to cooperative robot structures [22] or fast genetic algorithm for robot soccer and planet exploration citelee. We consider that this direction of research may find applicability in the case of parallel robots, for the potential offered in solving many complex problems such as those in the case of parallel robot control discussed in this paper.…”

confidence: 99%

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

Moldovan

Grif

Gligor

2015

INT J COMPUT COMMUN

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This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6-PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.

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confidence: 99%

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

Moldovan

Grif

Gligor

2015

INT J COMPUT COMMUN

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“…At present, some studies [20] about how to organize events and events relationship have been investigated. However, less studies aim to mine some meaningful information from the events data of entities.…”

Section: Related Workmentioning

confidence: 99%

Mining Periodic Traces of an Entity on Web

Huang

Wang

Yan

et al. 2015

INT J COMPUT COMMUN

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A trace of an entity is a behavior trajectory of the entity. Periodicity is a frequent phenomenon for the traces of an entity. Finding periodic traces for an entity is essential to understanding the entity behaviors. However, mining periodic traces is of complexity procedure, involving the unfixed period of a trace, the existence of multiple periodic traces, the large-scale events of an entity and the complexity of the model to represent all the events. However, the existing methods can't offer the desirable efficiency for periodic traces mining. In this paper, Firstly, a graph model(an event relationship graph) is adopted to represent all the events about an entity, then a novel and efficient algorithm, TracesMining, is proposed to mine all the periodic traces. In our algorithm, firstly, the cluster analysis method is adopted according to the similarity of the activity attribute of an event and each cluster gets a different label, and secondly a novel method is proposed to mine all the Star patterns from the event relationship graph. Finally, an efficient method is proposed to merge all the Stars to get all the periodic traces. High efficiency is achieved by our algorithm through deviating from the existing edge-by-edge pattern-growth framework and reducing the heavy cost of the calculation of the support of a pattern and avoiding the production of lots of redundant patterns. In addition, our algorithm could mine all the large periodic traces and most small periodic traces. Extensive experimental studies on synthetic data sets demonstrate the effectiveness of our method.

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