Parallel computation of continually on-line trained neural networks for identification and control of induction motors

Rubaai, A.; Kotaru, R.; Kankam, M.D.

doi:10.1109/ias.1999.799173

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In the classification phase where neural networks are used to recognize the gesture image based on its extracted feature, we analyze some problems related to the recognition and convergence of the neural network algorithm. As a classification method, ANN has been widely employed especially for real-world applications because of its ability to work in parallel and online training [16]. Thus, an ANN has been a lively field of research [17][18][19][20], In addition, a comparison between the two feature extraction algorithms is carried out in terms of accuracy and processing time (computational cost).…”

Section: Scope Of the Studymentioning

confidence: 99%

Recent methods in vision-based hand gesture recognition

Badi

2016

Int J Data Sci Anal

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The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The main objective of this effort is to explore the utility of two feature extraction methods, namely, hand contour and complex moments to solve the hand gesture recognition problem by identifying the primary advantages and disadvantages of each method. Artificial neural network is built for the purpose of classification by using the back-propagation learning algorithm. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, namely, pre-processing, feature extraction, and classification. In the pre-processing stage, some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation problems (in some cases). The complex moments algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The back-propagation learning algorithm is employed in the multi-layer neural network classifier. The results show that hand contour method has a performance of (71.30 %) recognition, while complex moments have a better performance of (86.90 %) recognition rate.

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Section: Scope Of the Studymentioning

confidence: 99%

Recent methods in vision-based hand gesture recognition

Badi

2016

Int J Data Sci Anal

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“…ANN has widely been employed, especially for real-world applications because of its ability to work in parallel and online training [15]. Thus, an ANN has been a lively field of research [16,17].…”

Section: Key Securitymentioning

confidence: 99%

Encryption function on artificial neural network

Azawee

Husien

Yunus

2015

Neural Comput & Applic

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Six algorithms that design secrecy keys are used for digital image encryption. The privacy keys generated by stream cipher generators were tested for their randomness applying five tests of randomness. The images generated by applying each algorithm were tested for their regularity and residual intelligibility. The histograms for images ciphered by stream cipher schemes all have approximately flat histogram less information as compared to one-dimensional ciphering algorithms and threshold generator-based image ciphering scheme. Furthermore, the stream cipher schemes were effectively applied to the colored images of 256-color levels of true-color images (24 bit). In this paper, we try to decrypt automatically using artificial neural network by decryption through multilayer perceptron and radial basis function; networks were tested using the interface by calculating the error rates of decrypted images.

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Adaptive tracking controller for induction motor drives using online training of neural networks

Rubaai

Kankam

2000

IEEE Trans. on Ind. Applicat.

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Parallel computation of continually on-line trained neural networks for identification and control of induction motors

Cited by 4 publications

References 14 publications

Recent methods in vision-based hand gesture recognition

Recent methods in vision-based hand gesture recognition

Encryption function on artificial neural network

Adaptive tracking controller for induction motor drives using online training of neural networks

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