• Title, Summary, Keyword: Neural Network Analysis

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Displacement prediction in geotechnical engineering based on evolutionary neural network

  • Gao, Wei;He, T.Y.
    • Geomechanics and Engineering
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    • v.13 no.5
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    • pp.845-860
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    • 2017
  • It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

Neural Network Based Dissolved Gas Analysis Using Gas Composition Patterns Against Fault Causes

  • J. H. Sun;Kim, K. H.;P. B. Ha
    • KIEE International Transactions on Electrophysics and Applications
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    • v.3C no.4
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    • pp.130-135
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    • 2003
  • This study describes neural network based dissolved gas analysis using composition patterns of gas concentrations for transformer fault diagnosis. DGA samples were gathered from related literatures and classified into six types of faults and then a neural network was trained using the DGA samples. Diagnosis tests were performed by the trained neural network with DGA samples of serviced transformers, fault causes of which were identified by actual inspection. Diagnosis results by the neural network were in good agreement with actual faults.

A Study on the Applicability of Neural Network Model for Prediction of tee Apartment Market (아파트시장예측을 위한 신경망분석 적응가능성에 대한 연구)

  • Nam, Young-Woo;Lee, Jeong-Min
    • Korean Journal of Construction Engineering and Management
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    • v.7 no.2
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    • pp.162-170
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    • 2006
  • Neural network analysis is expected to enhance the forecasting ability for the real estate market. This paper reviews definition, structure, strengths and weaknesses of neural network analysis, and verifies the applicability of neural network analysis for the real estate market. Neural network analysis is compared with regression analysis using the same sample data. The analyses model the macroeconomic parameters that influence the sales price of apartments. The results show that neural network analysis provides better forecasting accuracy than regression analysis does, what confirms the applicability of neural network analysis for the real estate market.

Performance Analysis of Neural Network on Determining The Optimal Stand Management Regimes (임분의 적정 시업체계분석을 위한 Neural Network 기법의 적용성 검토)

  • Chung, Joo Sang;Roise, Joseph P.
    • Journal of Korean Society of Forest Science
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    • v.84 no.1
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    • pp.63-70
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    • 1995
  • This paper discusses applications of neural network to stand stocking control problems. The scope of this research was to develop a neural network model for finding optimal stand management regimes and examining the performance of the model for field application. Performance was analyzed in consideration of the number of training examples and structural aspects of neural network. Research on network performance was based on extensive optimization studies for pure longleaf pine(Pinus palustris) stands. For experimental purposes. an existing nonlinear even-aged stand optimization model with a whole-stand growth and yield simulator was used to generate data samples required for the performance analysis.

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A Fuzzy Neural Network Combining Wavelet Denoising and PCA for Sensor Signal Estimation

  • Na, Man-Gyun
    • Nuclear Engineering and Technology
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    • v.32 no.5
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    • pp.485-494
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    • 2000
  • In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

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Using Structural Changes to support the Neural Networks based on Data Mining Classifiers: Application to the U.S. Treasury bill rates

  • Oh, Kyong-Joo
    • 한국데이터정보과학회:학술대회논문집
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    • pp.57-72
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    • 2003
  • This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

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Simulator Output Knowledge Analysis Using Neural network Approach : A Broadand Network Desing Example

  • Kim, Gil-Jo;Park, Sung-Joo
    • Proceedings of the Korea Society for Simulation Conference
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    • pp.12-12
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    • 1994
  • Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

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Acoustic Impulse Method with Neural Network for Detection of Cracks in Eggshell (음향충격법과 인공신경망에 의한 파란 검출)

  • 최완규;조한근;백진하;장영창
    • Journal of Biosystems Engineering
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    • v.23 no.6
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    • pp.621-628
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    • 1998
  • In order to develop an inspection algorithm for an automatic eggshell inspection system, acoustic impulse response with neural network method was studied. An improved error backpropagation algorithm was selected as a loaming rule of neural network, and three layer network was chosen for the neural network architecture. Acoustic signals in time domain and theirs power spectrum were studied as the input to the neural network. The classification feasibility and success rate were investigated in terms of statistical analysis and neural network approach. As a result, the success rate was 95% with the statistical model having five independent variables. Among the neural network models studied, the power spectrum of acoustic signal as the input with 64 input neurons and the two impact data showed the success rate of 95.5% which was slightly higher than of statistical analysis.

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Flexural and axial vibration analysis of beams with different support conditions using artificial neural networks

  • Civalek, Omer
    • Structural Engineering and Mechanics
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    • v.18 no.3
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    • pp.303-314
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    • 2004
  • An artificial neural network (ANN) application is presented for flexural and axial vibration analysis of elastic beams with various support conditions. The first three natural frequencies of beams are obtained using multi layer neural network based back-propagation error learning algorithm. The natural frequencies of beams are calculated for six different boundary conditions via direct solution of governing differential equations of beams and Rayleigh's approximate method. The training of the network has been made using these data only flexural vibration case. The trained neural network, however, had been tested for cantilever beam (C-F), and both end free (F-F) in case the axial vibration, and clamped-clamped (C-C), and Guided-Pinned (G-P) support condition in case the flexural vibrations which were not included in the training set. The results found by using artificial neural network are sufficiently close to the theoretical results. It has been demonstrated that the artificial neural network approach applied in this study is highly successful for the purposes of free vibration analysis of elastic beams.

Comparison between Logistic Regression and Artificial Neural Networks as MMPI Discriminator (MMPI 분석도구로서 인공신경망 분석과 로지스틱 회귀분석의 비교)

  • Lee, Jaewon;Jeong, Bum Seok;Kim, Mi Sug;Choi, Jee Wook;Ahn, Byung Un
    • Korean Journal of Biological Psychiatry
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    • v.12 no.2
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    • pp.165-172
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    • 2005
  • Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

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