• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1020-1024
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• 2005

In this paper, an approach to dynamic systems control is addressed based on exploiting the potential features of the new nonlinear neural oscillator. Neural oscillators have recently enabled robots to exhibit natural dynamics using their robustness and entrainment properties. To technically accomplish this objective, the neural oscillator should be connected to the robot joints under the sensory feedback. This also requires the neural oscillator to adapt to the non-periodic nature of arbitrary input patterns. However, even in the most widely-used Matsuoka oscillator, when an unknown quasi-periodic or non-periodic signal is applied, its output signal is not always closely entrained. Therefore, current neural oscillators may not be applied to the precise control of the dynamic systems response. We illustrate the enhanced entrainment properties of the new neural oscillator by numerical simulation and show the possibility for implementation to control a variety of dynamic systems. It is verified that the oscillator can produce rhythmic signals for generating actuator signals which can be naturally modified by incorporating sensory feedback to adapt to outer circumstances.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.347-353
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• 2004

Neural oscillator can be applied to oscillator systems such as analysis of image information, voice recognition and etc, Conventional learning algorithms(Neural Network or EBPA(Error Back Propagation Algorithm)) are not proper for oscillatory systems with the complicate input patterns because of its too much complex structure. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(phase locked loop) function and a simple Hebbian learning rule, Therefore, in this paper, it will introduce an technique for Recognition of the Korean Character using Phase Synchronization Neural Oscillator and will show the result of simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.315-317
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• 2003

Neural oscillator is applied in oscillatory systems (Analysis of image information, Voice recognition. Etc...). If we apply established EBPA(Error back Propagation Algorithm) to oscillatory system, we are difficult to presume complicated input's patterns. Therefore, it requires more data at training, and approximation of convergent speed is difficult. In this paper, I studied the neural oscillator as synchronized states with appropriate phase relation between neurons and recognized the Korean alphabet using Neural Oscillator Phase model Synchronization.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.491-494
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• 2004

Neural Oscillator can be applied to oscillatory systems such as the image recognition, the voice recognition, estimate of the weather fluctuation and analysis of geological fluctuation etc in nature and principally, it is used often to pattern recoglition of image information. Conventional BPL(Back-Propagation Learning) and MLNN(Multi Layer Neural Network) are not proper for oscillatory systems because these algorithm complicate Learning structure, have tedious procedures and sluggish convergence problem. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(phase-Locked Loop) function and by using a simple Hebbian learning rule. And also, Recognition velocity of Korean Character can be improved by using a Neural Oscillator's learning accelerator factor η$\_$ij/

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.93-99
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• 2004

Neural oscillator can be applied to oscillatory systems such as analyses of image information, voice recognition and etc. Conventional EBPA (Error back Propagation Algorithm) is not proper for oscillatory systems with the complicate input`s patterns because of its tedious training procedures and sluggish convergence problems. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(Phase Locked Loop) function and by using a simple Hebbian learning rule. Therefore, in this paper, a technique for Recognition of the Korean Alphabet using Phase Synchronized Neural Oscillator was introduced.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.275-281
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• 2003

A new neural oscillator has been designed and fabricated in an 0.5 ${\mu}{\textrm}{m}$ double poly CMOS technology. The proposed neural oscillator consists of a nonlinear variable resistor with negative resistance as well as simple transconductors and capacitors. The variable negative resistor which is used as a input stage of the oscillator consists of a positive feedback transconductors and a bump circuit with Gaussian-like I-V curve. The proposed neural oscillator has designed in integrated circuit with SPICE simulations. Simulations of a network of 4 oscillators which are connected with excitatory and inhibitory synapses demonstrate cooperative computation. Measurements of the fabricated oscillator chip with a $\pm$ 2.5 V power supply is shown and compared with the simulated results.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.103-106
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• 2003

An oscillatory neural network circuit has been designed and fabricated in an 0.5 ${\mu}{\textrm}{m}$ double poly CMOS technology. The proposed oscillatory neural network consists of 3 neural oscillator cells with excitatory synapses and a neural oscillator cell with inhibitory synapse. Simulations of a network of oscillators demonstrate cooperative computation. Measurements of the fabricated chip in condition of $\pm$ 2.5 V power supply is shown.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.24-29
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• 2006

A new oscillatory neural circuit with computational function has been designed and been designed and fabricated in an $0.5{\mu}m$ double poly CMOS technology. The proposed oscillatory circuit consists of 3 neural oscillators with excitatory synapses and a neural oscillator with inhibitory synapse. The oscillator block which is a basic element of the neural circuit is designed with a variable negative resistor and 2 transconductors. The variable negative resistor which is used as a input stage of the oscillator consist of a bump circuit with Gaussian-like I-V curve. SPICE simulations of a designed neural circuit demonstrate cooperative computation. Measurements of the fabricated neural chip in condition of ${\pm}$ 2.5 V power supply are shown and compared with the simulated results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1625-1626
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• 2013

• Journal of Sensor Science and Technology
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• v.12 no.1
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• pp.51-56
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• 2003

We describe a charge pump circuit using VCO (voltage controlled oscillator) for storing information into local memories in neural networks. The VCO is used for adjusting the output voltage of the charge pump to the reference voltage and for reducing the fluctuation generated by the clocking scheme. The charge pump circuit is simulated by using Hynix 0.35um CMOS process parameters. The proposed charge pump operates properly regardless to the temperature and the supply voltage variation.