• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.386-396
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• 2002

This paper presents a neural network-based near-optimal routing algorithm. It employs a modified Hopfield Neural Network (MHNN) as a means to solve the shortest path problem. It uses every piece of information that is available at the peripheral neurons in addition to the highly correlated information that is available at the local neuron. Consequently, every neuron converges speedily and optimally to a stable state. The convergence is faster than what is usually found in algorithms that employ conventional Hopfield neural networks. Computer simulations support the indicated claims. The results are relatively independent of network topology for almost all source-destination pairs, which nay be useful for implementing the routing algorithms appropriate to multi -hop packet radio networks with time-varying network topology.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.936-938
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• 1999

In this Paper, the design of a feedback linearization controller using multilayer neural network is proposed. The Proposed feedback linearization control scheme is designed by finding Lie derivatives from an identified neural networks. Lie derivatives are expressed as a combination of weights and neuron outputs. The proposed method is applied to an antenna arm problem and the simulation results show performance comparisons between the ordinary feedback linearization and the Proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.124-126
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• 1989

Inverse kinematic problem is a crucial point for robot manipulator control. In this paper, to implement the Jacobian control technique we used the Hopfield(Tank)'s neural network. The states of neurons represent joint veocities, and the connection weights are determined from the current value of the Jacobian matrix. The network energy function is constructed so that its minimum corresponds to the minimum least square error. At each sampling time, connection weights and neuron states are updated according to current joint position. Inverse kinematic solution to the planar redundant manipulator is solved by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.861-865
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• 1991

In general, the fusion of neural network and fuzzy logic theory is based on the fact that neural network and fuzzy logic theory have the common properties that 1) the activation function of a neuron is similar to the membership function of fuzzy variable, and 2) the functions of summation and products of neural network are similar to the Max-Min operator of fuzzy logics. In this paper, a fuzzy-neural network will be proposed and a force controller of the robot gripper, utilizing the fuzzy-neural network, will be presented. The effectiveness of the proposed strategy will be demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.24-29
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• 1990

In this paper ,ie propose a hybrid position/force controller of a robot manipulator using double-layer neural network. Each layer is constructed from inverse dynamics and Jacobian transpose matrix, respectively. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using a PUMA 560 manipulator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.897-903
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• 1991

In this paper we propose a hybrid position/force controller of a robot manipulator using feedback error learning rule and neural networks. The neural network is constructed from inverse dynamics. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained well, it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using PUMA 560 manipulator.

• Dementia and Neurocognitive Disorders
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• v.17 no.3
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• pp.83-89
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• 2018

Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron. The purpose of this review is to explain the fundamental concepts of artificial neural networks.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.302-307
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• 2005

A novel neuro controller based simple neuro-structure with modified error function is introduced in this paper. This controller consists of two independent controllers, known as the voltage regulator and the angular controller. The voltage regulator is used to modify terminal voltage for the purpose of tracking a reference voltage. The angular controller is utilized to guarantee the stability of the system. In this structure each neuron uses a linear hard limit activation function that depends on the controlled variable and its derivatives. There is no need for parameter identification or any off-line training data. Two proposed controllers are merged by a smooth switch to build a complete controller. The effectiveness of the proposed novel control action is demonstrated through some computer simulations on a Single-Machine Infinite-Bus (SMIB) power system.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.780-783
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• 2007

A Many studies have been made on the prediction of human voluntary movement intention in real-time based on invasive or non-invasive methods to help severely motor-disabled persons by offering some abilities of motor controls and communications. In the present study, we have developed an internet game driven by and/or linked to a brain-computer interface (BCI) system. Activities of two single neuronal units recorded from either hippocampus or prefrontal cortex of SD rats were used in real time to control two-dimensional movements of a robot, or a game object.

• Proceedings of the IEEK Conference
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• summer
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• pp.417-420
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• 2004

Caenorhabditis (C) elegans is often used in genetic analysis in neuroscience because its simple organism; an adult hermaphrodite contains only 302 neuron. So the worm is often used to study of cancer, alzheimer disease, aging, etc. To analysis mutant type of the worm, an experienced observer was able to subjectively before, but requirements for objective analysis are now increasing. For this reason, we use automated tracking systems to extract global movement coordinate of the worm. In this paper, we extract features, which are related on reversal and movement of the worm. Using these features, we quantitatively analysis 6 type mutant by movement and reversal characteristic.