• Title/Summary/Keyword: Data Optimization

Search Result 3,460, Processing Time 0.032 seconds

The optimization of fuzzy neural network using genetic algorithms and its application to the prediction of the chaotic time series data (유전 알고리듬을 이용한 퍼지 신경망의 최적화 및 혼돈 시계열 데이터 예측에의 응용)

  • Jang, Wook;Kwon, Oh-Gook;Joo, Young-Hoon;Yoon, Tae-Sung;Park, Jin-Bae
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1997.10a
    • /
    • pp.708-711
    • /
    • 1997
  • This paper proposes the hybrid algorithm for the optimization of the structure and parameters of the fuzzy neural networks by genetic algorithms (GA) to improve the behaviour and the design of fuzzy neural networks. Fuzzy neural networks have a distinguishing feature in that they can possess the advantage of both neural networks and fuzzy systems. In this way, we can bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level, human like IF-THEN rule thinking and reasoning of fuzzy systems into neural networks. As a result, there are many research works concerning the optimization of the structure and parameters of fuzzy neural networks. In this paper, we propose the hybrid algorithm that can optimize both the structure and parameters of fuzzy neural networks. Numerical example is provided to show the advantages of the proposed method.

  • PDF

Multi-Objective Design Exploration for Multidisciplinary Design Optimization Problems

  • Obayashi Shigeru;Jeong Shinkyu;Chiba Kazuhisa
    • 한국전산유체공학회:학술대회논문집
    • /
    • 2005.10a
    • /
    • pp.1-10
    • /
    • 2005
  • A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization (MDO) problems by CFD-CSD coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of Kriging Model, Adaptive Range Multi Objective Genetic Algorithms, Analysis of Variance and Self-Organizing Map. The main emphasis of this approach is visual data mining. An MDO system using high fidelity simulation codes, Navier-Stokes solver and NASTRAN, has been developed and applied to a regional-jet wing design. Because the optimization system becomes very computationally expensive, only brief exploration of the design space has been performed. However, data mining result demonstrates that design knowledge can produce a good design even from the brief design exploration.

  • PDF

Applications of Soft Computing Techniques in Response Surface Based Approximate Optimization

  • Lee, Jongsoo;Kim, Seungjin
    • Journal of Mechanical Science and Technology
    • /
    • v.15 no.8
    • /
    • pp.1132-1142
    • /
    • 2001
  • The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods 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 training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

  • PDF

Diversification, performance and optimal business mix of insurance portfolios

  • Kim, Hyun Tae
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.6
    • /
    • pp.1503-1520
    • /
    • 2013
  • For multi-line insurance companies, allocating the risk capital to each line is a widely-accepted risk management exercise. In this article we consider several applications of the Euler capital allocation. First, we propose visual tools to present the diversification and the line-wise performance for a given loss portfolio so that the risk managers can understand the interactions among the lines. Secondly, on theoretical side, we prove that the Euler allocation is the directional derivative of the marginal or incremental allocation method, an alternative capital allocation rule in the literature. Lastly, we establish the equivalence between the mean-shortfall optimization and the RORAC optimization when the risk adjusted capital is the expected shortfall, and show how to construct the optimal insurance business mix that maximizes the portfolio RORAC. An actual loss sample of an insurance portfolio is used for numerical illustrations.

Metaheuristics for reliable server assignment problems

  • Jang, Kil-Woong;Kim, Jae-Hwan
    • Journal of Advanced Marine Engineering and Technology
    • /
    • v.38 no.10
    • /
    • pp.1340-1346
    • /
    • 2014
  • Previous studies of reliable server assignment considered only to assign the same cost of server, that is, homogeneous servers. In this paper, we generally deal with reliable server assignment with different server costs, i.e., heterogeneous servers. We formulate this problem as a nonlinear integer programming mathematically. Our problem is defined as determining a deployment of heterogeneous servers to maximize a measure of service availability. We propose two metaheuristic algorithms (tabu search and particle swarm optimization) for solving the problem of reliable server assignment. From the computational results, we notice that our tabu search outstandingly outperforms particle swarm optimization for all test problems. In terms of solution quality and computing time, the proposed method is recommended as a promising metaheuristic for a kind of reliability optimization problems including reliable sever assignment and e-Navigation system.

Application of Multivariate Adaptive Regression Spline-Assisted Objective Function on Optimization of Heat Transfer Rate Around a Cylinder

  • Dey, Prasenjit;Das, Ajoy K.
    • Nuclear Engineering and Technology
    • /
    • v.48 no.6
    • /
    • pp.1315-1320
    • /
    • 2016
  • The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.

Personalized Web Service Recommendation Method Based on Hybrid Social Network and Multi-Objective Immune Optimization

  • Cao, Huashan
    • Journal of Information Processing Systems
    • /
    • v.17 no.2
    • /
    • pp.426-439
    • /
    • 2021
  • To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

L1-penalized AUC-optimization with a surrogate loss

  • Hyungwoo Kim;Seung Jun Shin
    • Communications for Statistical Applications and Methods
    • /
    • v.31 no.2
    • /
    • pp.203-212
    • /
    • 2024
  • The area under the ROC curve (AUC) is one of the most common criteria used to measure the overall performance of binary classifiers for a wide range of machine learning problems. In this article, we propose a L1-penalized AUC-optimization classifier that directly maximizes the AUC for high-dimensional data. Toward this, we employ the AUC-consistent surrogate loss function and combine the L1-norm penalty which enables us to estimate coefficients and select informative variables simultaneously. In addition, we develop an efficient optimization algorithm by adopting k-means clustering and proximal gradient descent which enjoys computational advantages to obtain solutions for the proposed method. Numerical simulation studies demonstrate that the proposed method shows promising performance in terms of prediction accuracy, variable selectivity, and computational costs.

New Optimization Algorithm for Data Clustering (최적화에 기반 한 데이터 클러스터링 알고리즘)

  • Kim, Ju-Mi
    • Journal of Intelligence and Information Systems
    • /
    • v.13 no.3
    • /
    • pp.31-45
    • /
    • 2007
  • Large data handling is one of critical issues that the data mining community faces. This is particularly true for computationally intense tasks such as data clustering. Random sampling of instances is one possible means of achieving large data handling, but a pervasive problem with this approach is how to deal with the noise in the evaluation of the learning algorithm. This paper develops a new optimization based clustering approach using an algorithm specifically designed for noisy performance. Numerical results show this algorithm better than the other algorithms such as PAM and CLARA. Also with this algorithm substantial benefits can be achieved in terms of computational time without sacrificing solution quality using partial data.

  • PDF

Efficient Mission Data Transmission with Sampling-Based Optimization in MIL-STD-1553B (MIL-STD-1553B 통신에서 샘플링 기반 최적화 기법을 이용한 효율적 임무 자료 전송)

  • Lee, Heoncheol;Kim, Kipyo;Kwon, Yongsung
    • Journal of the Korea Institute of Military Science and Technology
    • /
    • v.21 no.3
    • /
    • pp.370-378
    • /
    • 2018
  • The mission data in missile systems should be quickly and reliably transmitted from a mission transmission device to a guidance control unit. The MIL-STD-1553B is one of the reliable communication standards, but its bit rate is generally limited to 1Mbps due to the intrinsic properties of its electrical design. Therefore, the bus controller needs to be optimized to efficiently transmit the mission data on the inevitably limited bit rate. This paper proposes an analytical approach based on sampling-based optimization methods to maximize the data throughput without data loss. The proposed approach was evaluated in the simulations with the data transmission model for the MIL-STD-1553B communication system. The results of the proposed methods were applied to a real-time system and showed that the proposed method was successfully performed.