• Title/Summary/Keyword: neural network optimization

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Neural network analysis of water pollution for a main river, Tamagawa, in Tokyo metropolis

  • Yuan, Yan;Kambe, Junko;Aoyama, T.;Nagashima, U.
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1078-1083
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    • 2004
  • We proposed a method to compensate incomplete observations and made a study of environmental problem, water quality of Tama-River in Tokyo.The method is based on interpolations of the multi-layer neural networks. We call the approach as CQSAR method .which can compensate the defect data.The water quality data include defects which will give wrong effect to other normal data. The CQSAR method suppresses the wrong effect .Thus, we believe that the proposed CQSAR method has practical usability for environment examinations.

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On Designing A Fuzzy-Neural Network Control System Combined with Genetic Algorithm (유전알고리듬을 결합한 퍼지-신경망 제어 시스템 설계)

  • 김용호;김성현;전홍태;이홍기
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.8
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    • pp.1119-1126
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    • 1995
  • The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

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Cross-Validation Probabilistic Neural Network Based Face Identification

  • Lotfi, Abdelhadi;Benyettou, Abdelkader
    • Journal of Information Processing Systems
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    • v.14 no.5
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    • pp.1075-1086
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    • 2018
  • In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.

Optimization of Reactive Ion Etching of Benzocyclobutene Using Neural Networks (Benzocyclobutene에 대한 Reactive Ion Etching의 최적화)

  • Park, Bo-Hyeon;Soh, Dea-Wha;Hong, Sang-Jeen
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2006.06a
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    • pp.188-189
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    • 2006
  • 차세대 반도체 공정을 위한 많은 노력 중 미세가공의 중요성이 날로 증가함에 따라 reactive ion etching (RIE)에 대한 연구 또한 그 중요성이 커지고 있다. 본 논문에서는 RIE 과정에서 etch rate과 uniformity에 영향을 줄 수 있는 요인 4가지 즉, $CHF_3$, $O_2$, chamber pressure, RF power의 변화에 대한 실험 계획법(DOE)을 통해 계획하고, 실험한 후 neural network를 통해 학습함으로서 RIE 공정상의 최적화를 모색하였다.

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Problem Solution of Linear Programming based Neural Network

  • Son, Jun-Hyug;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 2004.05a
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    • pp.98-101
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    • 2004
  • Linear Programming(LP) is the term used for defining a wide range of optimization problems in which the objective function to be minimized or maximized is linear in the unknown variables and the constraints are a combination of linear equalities and inequalities. LP problems occur in many real-life economic situations where profits are to be maximized or costs minimized with constraint limits on resources. While the simplex method introduced in a later reference can be used for hand solution of LP problems, computer use becomes necessary even for a small number of variables. Problems involving diet decisions, transportation, production and manufacturing, product mix, engineering limit analysis in design, airline scheduling, and so on are solved using computers. This technique is called Sequential Linear Programming (SLP). This paper describes LP's problems and solves a LP's problems using the neural networks.

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Minimization of differential column shortening and sequential analysis of RC 3D-frames using ANN

  • Njomo, Wilfried W.;Ozay, Giray
    • Structural Engineering and Mechanics
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    • v.51 no.6
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    • pp.989-1003
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    • 2014
  • In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.

Design of Fuzzy Neural Networks Based on Fuzzy Clustering with Uncertainty (불확실성을 고려한 퍼지 클러스터링 기반 퍼지뉴럴네트워크 설계)

  • Park, Keon-Jun;Kim, Yong-Kab;Hoang, Geun-Chang
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.17 no.1
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    • pp.173-181
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    • 2017
  • As the industries have developed, a myriad of big data have been produced and the inherent uncertainty in the data has also increased accordingly. In this paper, we propose an interval type-2 fuzzy clustering method to deal with the inherent uncertainty in the data and, using this method, design and optimize the fuzzy neural network. Fuzzy rules using the proposed clustering method are designed and carried out the learning process. Genetic algorithms are used as an optimization method and the model parameters are optimally explored. Experiments were performed with two pattern classification, both of the experiments show the superior pattern recognition results. The proposed network will be able to provide a way to deal with the uncertainty increasing.

Experimental calibration of forward and inverse neural networks for rotary type magnetorheological damper

  • Bhowmik, Subrata;Weber, Felix;Hogsberg, Jan
    • Structural Engineering and Mechanics
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    • v.46 no.5
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    • pp.673-693
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    • 2013
  • This paper presents a systematic design and training procedure for the feed-forward back-propagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output, an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where the observed modeling errors for the inverse model can be related to knocking effects in the measured force due to the bearing plays between hydraulic piston and MR damper rod. Finally, the validated models are used to emulate pure viscous damping. Comparison of numerical and experimental results demonstrates good agreement in the post-yield region of the MR damper, while the main error of the inverse NN occurs in the pre-yield region where the inverse NN overestimates the current to track the desired viscous force.

A Study on Improving Formability of Stamping Processes with Segmented Blank Holders using Artificial Neural Network and Genetic Algorithm (인공신경망과 유전 알고리즘을 이용한 분할 블랭크 홀더 스탬핑 공정의 성형성 향상에 관한 연구)

  • G. P. Kim;S. D., Goo;M. S. Kim;G. M. Han;S. W. Jun;J. S. Lee;J. H. Kim
    • Transactions of Materials Processing
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    • v.32 no.5
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    • pp.276-286
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    • 2023
  • The field of sheet metal forming using press technology has become essential in modern mass production systems. Draw bead is often used to enhance formability. However, optimal draw bead design often requires excessive time and cost due to iterative experimentation and sometimes results in some defects. Given these challenges, there is a need to enhance formability by introducing segmented blank holders without draw beads. In this paper, the feasibility of a localized holding strategy using segmented blank holders is evaluated without the use of draw beads. The possibility for improving the formability was evaluated by utilizing a combination of the forming limit diagram and the wrinkle pattern-based defect indicators. Artificial neural networks were used for predicting defect indicators corresponding to arbitrary input holding forces and the NSGA-II optimization algorithm is used to find optimum blank holder forces yielding better defect indicators than the original process with drawbeads. Using optimum holding forces obtained from the proposed procedure, the stamping process with the segmented blank holders can yield better formability than the conventional process with drawbeads.

Concrete Optimum Mixture Proportioning Based on a Database Using Convex Hulls (최소 볼록 집합을 이용한 데이터베이스 기반 콘크리트 최적 배합)

  • Lee, Bang-Yeon;Kim, Jae-Hong;Kim, Jin-Keun
    • Journal of the Korea Concrete Institute
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    • v.20 no.5
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    • pp.627-634
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    • 2008
  • This paper presents an optimum mixture design method for proportioning a concrete. In the proposed method, the search space is constrained as the domain defined by the minimal convex region of a database, instead of the available range of each component and the ratio composed of several components. The model for defining the search space which is expressed by the effective region is proposed. The effective region model evaluates whether a mix-proportion is effective on processing for optimization, yielding highly reliable results. Three concepts are adopted to realize the proposed methodology: A genetic algorithm for the optimization; an artificial neural network for predicting material properties; and a convex hull for evaluating the effective region. And then, it was applied to an optimization problem wherein the minimum cost should be obtained under a given strength requirement. Experimental test results show that the mix-proportion obtained from the proposed methodology using convex hulls is found to be more accurate and feasible than that obtained from a general optimum technique that does not consider this aspect.