• 통합자연과학논문집
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• 제12권4호
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1222-1225
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• 1993

The purpose of this paper is to construct a prediction system on the chance of rain in a local region using a fuzzy relational model. The prediction system consists of two parts. One is a prediction part on the chance of rain. The compositional law of fuzzy inference, proposed by Zadeh, is applied to predict the chance of rain. The other is a learning part of a fuzzy relational model using input-output data. A simple and fast learning algorithm is used in this part. Simulations are carried out by the actual weather data in our city and their results show the validity of prediction by the fuzzy relational approach.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.47-52
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• 2023

In this paper a new fuzzy prediction is designed and developed to predict the type of delivery based on 7 factors. The developed system is highly needed to give a recommendation to the family excepting baby and at the same time provide an advisory system to the physician. The system has been developed using MATLAB and has been tested and verified using real data. The system shows high accuracy 95%. The results has been also checked one by one by a physician. The system shows perfect matching with the decision of the physician.

• 전기학회논문지
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• 제59권1호
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• 한국지능시스템학회논문지
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• 제10권2호
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• pp.113-121
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• 2000

이 논문에서는 병렬구조 퍼지시스템(PSFS)에 기초한 카오스 시계열 데이터의 예측 알고리즘에 대해 연구하였다 병렬구조 퍼지시스템은 병렬로 연결된 여러개의 퍼지시스템에 의하여 구성되어있다. 병렬구조 퍼지시스템을 구성하고 있는 각 퍼지시스템은 다른 임베딩 차원과 시간지연을 가지고 과거의 데이터를 이용하여 동일한 데이터를 독립적으로 예측한다 퍼지시스템은 입출력 데이터를 클러스터링하여 모델링되는 MISO Sugeno 퍼지규칙에 의하여 특징지어진다. 각 퍼지시스템에 대한 최적 임베딩차원은 주어진 시간지연값에 대해서 최적의 성능을 갖도록 선정된다. 병렬구조 퍼지시스템은 각 구성요소 퍼지스템들의 예측값중에서 최대값과 최소값을 가지는 예측결과를 제외하고 나머지 값들을 평균하여 최종 예측 결과를 얻는다.

• 디지털융복합연구
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• 제17권1호
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• pp.239-247
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• 2019

감정을 정확히 예측하는 것은 환자중심의 의료디바이스 개발 및 감성관련 산업에서 매우 중요한 이슈이다. 감정예측에 관한 많은 연구 중 감정 예측에 심박 변동성과 뉴로-퍼지 접근법을 적용한 연구는 없다. 본 연구는 HRV를 이용한 ANFEP(Adaptive Neuro Fuzzy system for Emotion Prediction)을 제안한다. ANFEP의 핵심 기능은 인공 신경망과 퍼지시스템을 통합해 예측 모델을 학습하는 ANFIS(Adaptive Neuro-Fuzzy Inference System)에 기반한다. 제안 모형의 검증을 위해 50명의 실험자를 대상으로 청각자극으로 감정을 유발하고, 심박변이도를 구하여 ANFEP 모형에 입력하였다. STDRR과 RMSSD를 입력으로 하고 입력변수 당 2개의 소속함수로 하는 ANFEP모형이 가장 좋은 결과를 나타났다. 제안한 감정예측 모형을 선형회귀 분석, 서포트 벡터 회귀, 인공신경망, 랜덤 포레스트와 비교한 결과 본 제안모형이 가장 우수한 성능을 보였다. 연구 결과는 보다 적은 입력으로 신뢰성 높은 감정인식이 가능함을 입증했고, 이를 활용해 보다 정확하고 신뢰성 높은 감정인식 시스템 개발에 대한 연구가 필요하다.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권6호
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권4호
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• pp.393-401
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• 2000

본 논문은 주어진 고장 데이타로부터 소프트웨어의 신뢰성 예측력 향상을 위해 뉴로-퍼지 시스템 연구를 수행하였다. 다른 소프트웨어로부터 수집된 10개의 고장 수 데이타와 4개의 고장시간 데이타에 대해 규칙의 수를 변경시키면서 다음 단계 예측을 실험하였다. 뉴로-퍼지 시스템의 예측력을 보이기 위해 다음 단계 예측에 대해 비교하였다. 실험 결과 뉴로-퍼지 시스템은 다양한 소프트웨어에 잘 적용되었다. 또한 널리 사용되고 있는 신경망과 통계적 소프트웨어 신뢰성 성장 모델의 예측력과 견줄 정도의 좋은 결과를 얻었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(3)
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• pp.5-8
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• 2000

In this paper, we propose new algorithms for the partition of input space and the generation of fuzzy control rules. The one consists of Shannon and extended fuzzy entropy function, the other consists of adaptive fuzzy neural system with back propagation teaming rule. The focus of this scheme is to realize the optimal fuzzy rule base with the minimal number of the parameters of the rules, reducing the complexity of the system. The proposed algorithm is tested with the time series prediction problem using Mackey-Glass chaotic time series.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권5호
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• pp.233-240
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• 2001

This paper presents a time series prediction method using a fuzzy rule-based system. Extracting fuzzy rules by performing a simple one-pass operation on the training data is quite attractive because it is easy to understand, verify, and extend. The simplest method is probably to relate an estimate, x(n+k), with past data such as x(n), x(n-1), ..x(n-m), where k and m are prefixed positive integers. The relation is represented by fuzzy if-then rules, where the past data stand for premise part and the predicted value for consequence part. However, a serious problem of the method is that it cannot handle nonstationary data whose long-term mean is varying. To cope with this, a new training method is proposed, which utilizes the difference of consecutive data in a time series. In this paper, typical previous works relating time series prediction are briefly surveyed and a new method is proposed to overcome the difficulty of prediction nonstationary data. Finally, computer simulations are illustrated to show the improved results for various time series.