Using evolutionary neural networks to predict spatial orientation of a ship

Praczyk, Tomasz

doi:10.1016/j.neucom.2015.03.075

Cited by 15 publications

(10 citation statements)

References 12 publications

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“…The way of encoding ANN in the form of network definition matrix (NDM) (Praczyk 2015d) In AEEO, the operations with predefined implementations are replaced with ANNs-operations with the same task as in the classic variant of the method. Moreover, the data used previously by the operations are completely removed from the programs which, in AEEO, include exclusively the ANNsoperations.…”

Section: Fig 12mentioning

confidence: 99%

“…13). Otherwise, the item remains intact and the corresponding connection between neurons in the final ANN is not established (Praczyk 2015d).…”

Section: Fig 12mentioning

confidence: 99%

“…To form a complete solution, selected representatives (usually, the best ones) of each population are combined together. Application of this evolutionary scheme in AEEO consists (Praczyk 2015d) in evolution of individual ANNs-operations in separate populations. The number of ANNs-operations in a complete program corresponds to the number of populations (see Fig.…”

Section: Fig 12mentioning

confidence: 99%

“…Each population can also be replaced with a newly created population. Such a situation takes place when the influence of all ANNs-operations from a given population on fitness of generated solutions is definitely lower than the influence of ANNs-operations from the remaining populations (a population can be replaced when, for example, fitness of a population, measured as the average fitness of all ANNsoperations from the population, is definitely lower than the fitness of the remaining populations) (Praczyk 2015d).…”

Section: Fig 12mentioning

confidence: 99%

“…As before, transfer of the parameters is performed in a loop until all the elements in NDM have a value assigned. In the last phase, NDM is transformed into an ANN-operation (Praczyk 2015d).…”

Section: Fig 12mentioning

confidence: 99%

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Neural collision avoidance system for biomimetic autonomous underwater vehicle

Praczyk

2019

Soft Comput

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Autonomous underwater vehicles (AUVs) are underwater robots which are able to perform certain tasks without the help of a human operator. The key skill of each AUV is the capability to avoid collisions. To this end, appropriate devices and software are necessary with the potential to detect obstacles and to take proper decisions from the point of view of both the task and safety of the vehicle. The paper presents a neural collision avoidance system (NCAS) designed for the biomimetic autonomous underwater vehicle (BAUV). The NCAS is a component of the path following and collision avoidance system (PFCAS), which as the name implies is responsible for safely leading the vehicle along a desired path with collision avoidance. The task of NCAS is to make decisions regarding vehicle maneuvers in the horizontal plane, but only in the close proximity of the obstacles. It is implemented as an evolutionary artificial neural network designed by means of a neuro-evolutionary technique called assembler encoding with evolvable operations (AEEO). The paper outlines operation and construction of the BAUV as well as the PFCAS, the role of the NCAS in the entire system, and briefly presents AEEO as well as reporting on the experiments performed in simulation.

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Section: Fig 12mentioning

confidence: 99%

“…13). Otherwise, the item remains intact and the corresponding connection between neurons in the final ANN is not established (Praczyk 2015d).…”

Section: Fig 12mentioning

confidence: 99%

Section: Fig 12mentioning

confidence: 99%

Section: Fig 12mentioning

confidence: 99%

Section: Fig 12mentioning

confidence: 99%