• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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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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• 제28권2호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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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/

• 한국지능시스템학회논문지
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• 제14권1호
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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.

• 전자공학회논문지SC
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• 제40권4호
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• pp.275-281
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• 2003

0.5㎛ 2중 폴리 CMOS 공정을 이용하여 새로운 뉴럴 오실레이터를 설계, 제작하였다. 제안하는 뉴럴 오실레이터는 트랜스콘덕터 및 캐패시터와 비선형 가변 부성저항으로 이루어진다. 뉴럴 오실레이터의 입력단으로 사용되는 비선형 가변 부성저항은 정귀환의 트랜스콘덕터와 가우시안 분포의 전류전압 특성을 지니는 범프 회로를 이용하여 구현하였다. 또한 SPICE 모의실험을 통하여 제안한 오실레이터의 특성분석 후 집적회로 설계를 실시하였다. 한편 흥분성 및 억제성 시냅스로 연결된 4개의 뉴럴 오실레이터로 간단한 신경회로망을 구성하여 그 특성을 확인하였다. 집적회로로 제작된 뉴럴 오실레이터에 대하여 ± 2.5 V 전원 조건하에서 측정된 결과를 분석하고 모의실험 결과와 비교한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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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.

• 한국지능시스템학회논문지
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• 제16권1호
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• pp.24-29
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• 2006

연산기능을 갖는 새로운 진동성 신경회로를 설계하여 $0.5{\mu}m$ CMOS 공정으로 칩 제작을 하였다. 제안하는 진동성 신경회로는 흥분성 시냅스를 가진 3개의 신경진동자와 억제성 시냅스를 가진 1개의 신경진동자로 이루어진다. 사용된 진동자는 가변 부성저항과 트랜스콘덕터를 이용하여 설계하였다. 진동자의 입력단으로 사용되는 가변 부성저항은 가우시안 분포의 전류전압 특성을 지니는 범프 회로를 이용하여 구현하였다. 뉴럴 회로의 SPICE 모의실험결과 간단한 연산기능을 확인하였다. 제작된 칩을 ${\pm}$ 2.5 V 의 전원전압 조건에서 측정하였고 이를 모의실험결과와 비교 분석하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.1625-1626
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• 2013

• 센서학회지
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• 제12권1호
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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.