• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• 대한기계학회논문집A
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• 제25권3호
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• pp.537-547
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• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.495-500
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• 1998

This paper introduces our three approaches to reduce the burden of human interactive EC operators: (1) improvement of the interface of presenting individuals, (2)improvement of the interface of inputting fitness values, and (3) fast EC convergence. We propose methods to display individuals in order of predicted fitness values by neural networks or Euclidean distance measure for (1), to input quantized fitness values for (2), and to make a new elite by approximating the EC search space with a quadratic function for (3). They are evaluated through simulations and subjective testes, and their effects have shown.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.265-268
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• 2004

본 논문은 최적화 방법인 유전자 알고리즘을 이용하여 진화 컴퓨팅 기반 RBF 신경회로망을 이용한 새로운 비선형 시스템 설계 방법을 제안한다. 비선형 시스템 설계시 문제점으로는 복잡성과 불확실성을 들수 있으며, 이러한 문제를 해결하기 위해서 지능형 모델을 사용하게 되었다. 본 논문에서는 일반적인 신경회로망보다 성능이 뛰어난 RBF 신경회로망을 사용하여 비선형 시스템을 모델링 한다. HCM 클러스터링을 이용하여 유사한 특성을 가진 비선형 데이터를 분류하여 입력으로 사용한다. 제안한 진화 컴퓨팅 기반 RBF 신경회로망을 이용한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 학습 데이터와 테스트 데이터를 이용하여 그 우수성을 보인다.

• Management Science and Financial Engineering
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• 제15권2호
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• pp.101-118
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• 2009

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 봄 학술발표논문집 Vol.31 No.1 (A)
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• pp.262-264
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• 2004

시스템 호출 감사자료기반 기계학습기법을 사용한 프로그램 행위 학습방법은 효과적인 호스트기반 침입탐지 방법이며, 특히 신경망은 기존 연구 중 가장 좋은 성능을 보였다. 하지만 보통의 신경망은 그 구조를 찾기 위한 방법이 알려져 있지 않아 침입탐지에 효과적인 구조를 찾기 위해서는 많은 시간이 요구된다. 본 논문에서는 기존 신경망 기반 침입탐지시스템의 단점을 보완하고 성능을 향상시키기 위해 진화신경망을 이용한 방법을 제안한다. 진화 신경망은 신경망의 구조와 가중치를 동시에 학습하기 때문에 일반 신경망보다 빠른 시간 내에 더 좋은 성능의 신경망을 얻을 수 있다는 장점이 있다. 1999년의 DARPA IDEVAL자료로 실험한 결과 기존의 연구보다 좋은 성능을 보여 진화신경망이 침입탐지에 효과적임을 확인할 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.57-60
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• 1998

Darwin Machine은 자기 자신의 구조를 전자적인 속도로 진화해 나가는 하드웨어로서 복잡한 구조와 성질으 진화 기법을 사용하여 만들어 나가는 진화공학(Evolutionary Engineering)의 한 예이다. 하드웨어가 전자적인 속도로 진화하기 위해서는 각각으리 하드웨어 구성요소들이 병렬적으로 작동해햐 하는데 셀룰라 오토마타는 이러한 문제를 해결하는 적합한 구조이며, 하드췌어에 쉽게 이식할 수 있는 장점이 있다. 신경망의 학습 능력과 진한 연산을 이용하면 효율적인 진화를 유도할 수 있다. 본 논문에서는 이러한 하드웨어 구현을 위한 셀룰라 오토마타에 기반한 신경망을 보이고자 한다.

• The Korean Journal of Ecology
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• 제28권6호
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• pp.405-415
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• 2005

This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological models popularly adapted by many researchers have been a means of information processing in dealing with dynamics in various ecosystems. The up-to-date trend in ecological modelling partially turns to the application of ML to explain specific ecological events in complex ecosystems and to overcome the necessity of complicated data manipulation. This paper briefly introduces ML techniques applied to freshwater ecosystems in Korea. The manuscript provides promising information for the ecologists who utilize ML for elucidating complex ecological patterns and undertaking modelling of spatial and temporal dynamics of communities.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (2)
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• pp.733-735
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• 2003

DNA Microarray 기술은 유전자의 발현여부를 매우 빠르게 검사할 수 있는 도구이며 각종 질병의 발생여부를 예측하기 위한 정보를 제공한다. 유전자 발현 데이터로부터 암의 발생 여부를 예측하기 위해서는 기존의 접근방법과 다른 기계학습 기법이 요구된다. 일반적으로 샘플의 개수가 극히 적은 반면에 특징의 개수는 수천에서 수만 개가 존재하기 때문에 문제의 특성에 맞는 분류기의 구조를 결정하는 것이 매우 어려운 일이기 때문이다. 진화 신경망은 신경망의 구조와 가중치를 동시에 학습하며 사용자는 각 개체의 적합도를 평가할 수 있는 방법만 제공해 주면된다. 특히 신경망의 구조를 사전에 고정하지 않아도 되는 장점이 있기 때문에 전문적인 지식이 없는 사용자라도 이용가능하다. 대장암 데이터에 대한 실험결과 제안하는 분류기 모델이 다층 퍼셉트론, SVM (support vector machine), 최근접 이웃 방법에 비해 향상된 성능을 보였다.