• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 1998년도 The 12th Asia Quality Management Symposium* Total Quality Management for Restoring Competitiveness
• /
• pp.696-704
• /
• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
• /
• pp.569-571
• /
• 1995

This paper compares the simulated annealing and the Hopfield neural network method for an optimal routing in a multistage interconnection network(MIN). The MIN provides a multiple number of paths for ATM cells to avoid cell conflict. Exhaustive search always finds the optimal path, but with heavy computation. Although greedy method sets up a path quickly, the path found need not be optimal. The simulated annealing can find an sub optimal path in time comparable with the greedy method.

• 대한산업공학회지
• /
• 제24권2호
• /
• pp.251-260
• /
• 1998

This paper deals with a simulated annealing neural network model for determining sequences of models inputted into a mixed model assembly line. We first present a energy function fitting to our problem, next determine the value of the parameters of the energy function using convergence ratio and the number of searched feasible solution. Finally we compare our model NMS with the modified Thomopoulos model. The result of the comparison shows that NMS and Thomopoulos offer a similar output in the problems having good smoothness.

• 한국생산제조학회지
• /
• 제24권1호
• /
• pp.63-68
• /
• 2015

Pattern layout design is very important to the automation of apparel industry. Until now, the genetic algorithm and Tabu search method have been applied to layout design automation. With the genetic algorithm and Tabu search method, the obtained values are not always consistent depending on the initial conditions, number of iterations, and scheduling. In addition, the selection of various parameters for these methods is not easy. This paper presents a hybrid search method that uses a neural network and simulated annealing to solve these problems. The layout of pattern elements was optimized to verify the potential application of the suggested method to apparel pattern layout design.

• 대한기계학회논문집A
• /
• 제24권7호
• /
• pp.1753-1762
• /
• 2000

An inverse method is presented to obtain material's flow properties by using small punch test. This procedure employs, as the objective function of inverse analysis, the balance of measured load-di splacement response and calculated one during deformation. In order to guarantee convergence to global minimum, simulated annealing method was adopted to optimize the current objective function. In addition, artificial neural network was used to predict the load-displacement response under given material parameters which is the most time consuming and limits applications of global optimization methods to these kinds of problems. By implementing the simulated annealing for optimization along with calculating load-displacement curve by neural network, material parameters were identified irrespective of initial values within very short time for simulated test data. We also tested the present method for error-containing experimental data and showed that the flow properties of material were well predicted.

• Journal of Multimedia Information System
• /
• 제2권1호
• /
• pp.179-186
• /
• 2015

This paper describes the application of a simulated annealing to a random signal-based learning. The simulated annealing is used to generate the reinforcement signal which is used in the random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural network. It is poor at hill-climbing, whereas simulated annealing has an ability of probabilistic hill-climbing. Therefore, hybridizing a random signal-based learning with the simulated annealing can produce better performance than before. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. And the other is the optimization of fuzzy control rules using inverted pendulum.

• Journal of Electrical Engineering and information Science
• /
• 제3권3호
• /
• pp.342-347
• /
• 1998

An OptiNeural system is developed for optical pattern classification. It is a novel hybrid system which consists of an optical processor and a multilayer neural network. It takes advantages of two dimensional processing capability of an optical processor and nonlinear mapping capability of a neural network. The optical processor with a binary phase only filter is used as a preprocessor for feature extraction and the neural network is used as a decision system through mapping. OptiNeural system is trained for optical pattern classification by use of a simulated annealing algorithm. Its classification performance for grey tone texture patterns is excellent, while a conventional optical system shows poor classification performance.

• 대한기계학회논문집
• /
• 제17권12호
• /
• pp.3063-3072
• /
• 1993

The layout is an important and difficult problem in industrial applications like sheet metal manufacturing, garment making, circuit layout, plant layout, and land development. The module layout problem is known to be non-deterministic polynomial time complete(NP-complete). To efficiently find an optimal layout from a large number of candidate layout configuration a heuristic algorithm could be used. In recent years, a number of researchers have investigated the combinatorial optimization problems by using neural network principles such as traveling salesman problem, placement and routing in circuit design. This paper describes the application of Self-organizing Feature Maps(SOM) of the Kohonen network and Simulated Annealing Algorithm(SAA) to the layout problem of the two-dimensional rectangular shapes.

• Investigative Magnetic Resonance Imaging
• /
• 제6권2호
• /
• pp.137-146
• /
• 2002

목적: 본 논문에서는 신경망을 이용한 자기공명영상의 분류에 있어 결정론적 이완 방법(deterministic relaxation)과 응집 군집화(agglomerative clustering) 방법에 의한 개선된 영상 분류방법을 제시한다. 제안된 방법은 신경망을 이용한 영상의 분류시 지역적 최소치로의 수렴문제와 입력 패턴의 증대로 인하여 수렴 속가 늦어지는 문제를 해결한다. 대상 및 방법: 신경망을 이용한 영상의 분류는 지역적 계산과 병렬 계산이 가능한 특성을 갖고 있어 기존의 통계적 방법을 대신하는 방법으로 주목을 받고 있다. 그러나 일반적으로 신경망에 의한 분류알고리즘이 지닌 문제점의 하나는 에너지함수가 항상 전역적 최소치로 수렴하지 않고 지역적 최소치로도 수렴할 수 있다는 점이고, 또 다른 문제점은 반복수렴을 수행하는 에너지함수의 수렴속도가 너무 늦다는 점이다. 따라서 지역적 최소치로의 수렴을 방지하고 전역적 최소치로의 수렴속도를 가속화시키기 위하여 본 논문에서는 결정적 이완 알고리즘의 하나인 MFA(Mean Field Annealing) 방법을 적용하여 지역적 최소치로의 수렴문제를 해결하는 방법을 제시한다. MFA는 모의 애닐링의 통계적 성질을 변수의 평균값에 적용하는 결정론적인 수정 법칙들로 대신하고, 이러한 평균값을 최소화함으로서 수렴속도를 개선한 방법이다 아울러 신경망이 갖고 있는 문제점인 과다한 클래스 패턴의 생성에 따른 처리속도 지연의 문제점을 해결하기 위하여 응집 군집화 알고리즘을 이용하여 영상을 구성하는 군집을 결정하여 신경망에 입력되는 값을 초기화하여 영상패턴이 증가되는 것을 제한하였다. 결과: 본 논문에서 제시된 응집 군집화 방법 및 결정론적 이완 방법은 신경망에 의한 자기공명영상의 분류 시 발생할 수 있는 지역적 최적 치로의 수렴 문제를 해결하여 전역적 최적화로 신속히 수렴함을 알 수 있었다. 결론: 본 논문에서는 클러스터의 분석과 결정론적 이완 방법에 의하여 신경망에 의한 자기공명영상의 분류결과를 향상시키기 위한 새로운 방법을 소개하였으며 실험결과를 통하여 그러한 사실을 확인할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
• /
• pp.183-186
• /
• 1995

A method is presented for optimization of Universal Learning Networks (ULN), where, together with gradient method, Simulated Annealing (SA) is employed to elude local minima. The effectiveness of the method is shown by its application to control of a crane system.