• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.11a
• /
• pp.107-110
• /
• 2000

In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.1
• /
• pp.31-36
• /
• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.920-924
• /
• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.1
• /
• pp.39-42
• /
• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.381-383
• /
• 2009

본 논문에서는 대표적인 시스템 모델링 도구중의 하나인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)를 설계하고 모델을 최적화하기 위하여 최적화 알고리즘인 PSO(Particle Swarm Optimization) 알고리즘을 이용하였다. 즉, 모델의 최적화에 주요한 영향을 미치는 모델의 파라미터들을 PSO 알고리즘을 이용하여 동정한다. 제안된 RBF 뉴럴 네트워크는 은닉층에서의 활성함수로서 일반적으로 많이 사용되어지는 가우시안 커널함수를 사용한다. 더 나아가 모델의 최적화를 위하여 각 커널함수의 중심값은 HCM 클러스터링에 기반을 두어 중심값을 결정하고, PSO 알고리즘을 통하여 가우시안 커널함수의 분포상수, 은닉층에서의 노드 수 그리고 다수의 입력을 가질 경우 입력의 종류를 동정한다. 제안한 모델의 성능을 평가하기 위해 Mackey-Glass 시계열 공정 데이터를 적용하였으며 제안된 모델의 근사화와 일반화 능력을 분석한다.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.5
• /
• pp.812-820
• /
• 1995

This paper proposes an approach of identifying nonlinear mappings from input/output data. The approach is based on the universal approximation by the fuzzy integration of local affine mappings. A connectionist model realizing the universal approximator is suggested by using a processing unit based on both the radial basis function and the weighted sum scheme. In addition, a learning method with self-organizing capability is proposed for the identifying of nonlinear mapping relationships with the given input/output data. To show the effectiveness of our approach, the proposed model is applied to the function approximation and the prediction of Mackey-Glass chaotic time series, and the performances are compared with other approaches.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.280-280
• /
• 2000

In this paper, we propose a method of constructing equation using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is. we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.05
• /
• pp.265-269
• /
• 1995

In this study, we present a training method of radial basis function networks based on recursive modified Gram-Schmidt algorithm for single step prediction of chaotic time series. With single step predictions of Mackey-Glass time series and alpha-rhythm EEG which has chaotic characteristics, the radial basis function network trained by this method is compared with one trained by a classical non-recursive method and the radial basis function model proposed by X.D. He and A. Lapedes. The results show the effectiveness of the training method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.224-227
• /
• 2000

In this Paper, we prepose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series, Sun spot data and KOSPI data.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 1999.11a
• /
• pp.388-395
• /
• 1999

본 논문의 목적은 퍼지 엔트로피를 이용하여 비선형신호를 예측하는 것이다. 이 방법은 분할된 여러 부 공간(subspace)에 대해 입력 데이터로부터 퍼지 엔트로피를 이용하여 각각의 규칙에 등급을 정하여 불필요한 제어규칙을 제거하여 바람직한 규칙베이스를 구성하도록 한 것이다. 적용되는 퍼지 신경망의 기본적인 구조는 퍼지 제어기의 규칙베이스와 추론의 과정을 신경회로망을 이용하여 구현하며 퍼지 제어규칙의 매개변수들은 역전파 알고리즘에 의해 적응되어진다. 또한 매개변수의 수를 줄이기 위하여 제어규칙의 결론부의 출력값은 신경망의 가중치로 구성하였다. 결국 퍼지 신경망의 복잡도를 줄일 수 있다. Mackey-Glass 시계열의 예측에 대한 컴퓨터 시뮬레이션을 통하여 본 논문에서 제안한 방법의 효율성을 입증하고, 제안된 방법을 EEG 생리신호 분석에 이용될 수 있다.