The Experts below are selected from a list of 8007 Experts worldwide ranked by ideXlab platform
M. Kussul - One of the best experts on this subject based on the ideXlab platform.
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Neural Network System for face recognition
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004Co-Authors: E. Kussul, T. Baidyk, M. KussulAbstract:An image recognition method based on Neural Network System is proposed. This method uses the permutative coding technique for image preprocessing and Neural classifier for image recognition. We have proposed the permutative coding technique to make recognition process invariant to small displacements of the object in the image. The System was tested on the ORL database. This database contains 400 face images of 40 persons. 200 images are used for training and 200 for recognition. The error rate of 0.1% for face recognition was obtained. This method was tested also with 40, 80, 120 and 160 images for System training and the rest images for recognition. The error rates 16.1%, 7.09%, 2.15% and 1.4% were obtained respectively.
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ISCAS (5) - Neural Network System for face recognition
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004Co-Authors: E. Kussul, T. Baidyk, M. KussulAbstract:An image recognition method based on Neural Network System is proposed. This method uses the permutative coding technique for image preprocessing and Neural classifier for image recognition. We have proposed the permutative coding technique to make recognition process invariant to small displacements of the object in the image. The System was tested on the ORL database. This database contains 400 face images of 40 persons. 200 images are used for training and 200 for recognition. The error rate of 0.1% for face recognition was obtained. This method was tested also with 40, 80, 120 and 160 images for System training and the rest images for recognition. The error rates 16.1%, 7.09%, 2.15% and 1.4% were obtained respectively.
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A visual solution to modular Neural Network System development
Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2002Co-Authors: M. Kussul, A. Riznyk, E. Sadovaya, A. Sitchov, Tie-qi ChenAbstract:In this paper, a visual software tool (called MNN-CAD) for modular Neural Network System development is introduced. Its major functionalities such as constructing, training and testing a modular System are described. Its open-structure implementation is explained. Finally, the application of MNN-CAD in an automotive occupant spatial sensing problem is presented.
D.t. Macchiarolo - One of the best experts on this subject based on the ideXlab platform.
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An online Neural Network System for computer access security
IEEE Transactions on Industrial Electronics, 1993Co-Authors: D.t. MacchiaroloAbstract:A method for identifying computer users based on the individual typing techniques of the users is presented. The identification System is a pattern classification System based on a simulation of an artificial Neural Network. The user types a known sequence of characters, and the intercharacter times represent a pattern vector to be classified. This vector is presented to the classification System, and the pattern is assigned to a predefined class, thus identifying the user. The major work is divided into two phases: the investigation phase and the implementation phase. Experimental results are discussed, followed by a description of a real-time implementation of this System, using a personal computer, known as the OnLine User Identification System. In an operational trial, the System correctly identified users 97.8% of the time. This intelligent System can be used, in addition to the traditional user name and password procedures, to improve computer security in a cost-effective manner.
B.y. Chen - One of the best experts on this subject based on the ideXlab platform.
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A radical-partitioned Neural Network System using a modified sigmoid function and a weight-dotted radical selector for large-volume Chinese character recognition VLSI
Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 1994Co-Authors: B.y. ChenAbstract:This paper presents a radical-partitioned Neural Network System using a modified sigmoid function and a weight-dotted radical selector for large-volume Chinese characters recognition VLSI. With a modified sigmoid function and the weight-dotted radical selector, the recognition rate of 1000 radical-partitioned Chinese characters can be enhanced to 90% from 70% for the input samples with 15% random errors as compared to the System without it.
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A coded block Neural Network System suitable for VLSI implementation using an adaptive learning-rate epoch-based back propagation technique
1993 IEEE International Symposium on Circuits and Systems, 1993Co-Authors: B.y. ChenAbstract:A coded block adaptive Neural Network System is presented. It is suitable for VLSI implementation using an adaptive learning-rate epoch-based backpropagation technique to train large-volume input patterns. Using this coded block Neural Network System, 500 frequently-used Chinese characters were successfully trained in 47.2 hours using a 28 MIPs computer. Training of the epoch-based System is much less sensitive to initial weights and irrelevant to the order of the input patterns as compared to the System using the conventional backpropagation algorithm.
Haizhou Li - One of the best experts on this subject based on the ideXlab platform.
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A Spiking Neural Network System for Robust Sequence Recognition
IEEE Transactions on Neural Networks and Learning Systems, 2016Co-Authors: Qiang Yu, Huajin Tang, Haizhou LiAbstract:This paper proposes a biologically plausible Network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent System with functional parts of sensory encoding, learning, and decoding. This is the first Systematic model attempting to reveal the Neural mechanisms considering both the upstream and the downstream neurons together. The whole System is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the System is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the System is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the System provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The System structure would be beneficial for developments in both hardware and software.
T. Fujii - One of the best experts on this subject based on the ideXlab platform.
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Neural Network System for online controller adaptation and its application to underwater robot
Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), 1998Co-Authors: K. Ishii, T. FujiiAbstract:Describes a Neural Network System which executes identification of robot dynamics and controller adaptation in parallel with robot control. The System consists of two parts: real-world part and imaginary-world part. The real-world part is a feedback control System for the actual robot. In the imaginary-world part, the model of robot and the controller are adjusted continuously in order to deal with the change of dynamic property caused by disturbance and so on. The System is designed to be suitable for a computer System with parallel processing ability. In the paper, adaptability of the controller System is investigated by heading keeping and path following experiments on the condition that unknown disturbances are given to the robot.