• 대한의용생체공학회:의공학회지
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• 제22권4호
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• 대한공간정보학회지
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• 제4권2호
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• pp.137-147
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• 1996

본 논문에서는 통계적 분류방법인 최대유사 분류법(MLC: maximum likelihood classifier)과 신경회로망을 이용한 분류법인 다층퍼셉트론(MLP: multiayer perceptron) 분류법간의 분류성능을 비교 평가하였으며, 또한 MLP 분류법에서 문제가 되고 있는 학습률(learning rate), 운동량 상수(,momentum constant), 은닉층의 노드수에 따른 MLP 분류법의 분류성능을 평가하였다. 부산지역에 대한 실제 인공위성 화상데이타인 Landsat TM 화상데이타를 사용하여 MLP 분류법과 MLC 분류법의 성능을 비교한 결과 MLP 분류법의 성능이 더 우사함을 확인할 수 있었으며, 학습률, 운동량 상수 및 은닉층의 노드수에 따른 분류성능도 평가하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Journal of Biosystems Engineering
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• 제43권4호
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• pp.352-361
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• 2018

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

• Advances in environmental research
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• 제4권4호
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• Asian Journal of Atmospheric Environment
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• 제9권1호
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• pp.12-21
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• 2015

Air pollution is a challenging issue in some of the large cities in developing countries. In this regard, data interpretation is one of the most important parts of air quality management. Several methods exist to analyze air quality; among these, we applied the Multilayer Perceptron (MLP) and Radial Basis Function (RBF) methods to predict the hourly air concentration of benzene in 14 districts in the municipality of Tehran. Input data were hourly temperature, wind speed and relative humidity. Both methods determined reliable results. However, the RBF neural network performance was much closer to observed benzene data than the MLP neural network. The correlation determination resulted in 0.868 for MLP and 0.907 for RBF, while the Index of Agreement (IA) was 0.889 for MLP and 0.937 for RBF. The sensitivity analysis related to the MLP neural network indicated that the temperature had the greatest effect on prediction of benzene in comparison with the wind speed and humidity in the study area. The temperature was the most significant factor in benzene production because benzene is a volatile liquid.

• Smart Structures and Systems
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• 제33권1호
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.287-289
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• 2005

This paper presents a hybrid neural network system which used a Self-Organizing Map and Multilayer Perceptron for the problem of Malay syllables speech recognition. The novel idea in this system is the usage of a two-dimension Self-organizing feature map as a sequential mapping function which transform the phonetic similarities or acoustic vector sequences of the speech frame into trajectories in a square matrix where elements take on binary values. This property simplifies the classification task. An MLP is then used to classify the trajectories that each syllable in the vocabulary corresponds to. The system performance was evaluated for recognition of 15 Malay common syllables. The overall performance of the recognizer showed to be 91.8%.

• 대한산업공학회지
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• 제25권1호
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• 전자공학회논문지B
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• 제33B권8호
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• pp.105-113
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• 1996

In this paper, a new korean digita speech recognition technique was proposed using muktolayer perceptron (MLP). In spite of its weakness in dynamic signal recognition, MLP was adapted for this model, cecause korean syllable could give static features. It is so simle in its structure and fast in its computing that MLP was used to the suggested system. MLP's input vectors was transformed using karhunen-loeve transformation (KLT), which compress signal successfully without losin gits separateness, but its physical properties is changed. Because the suggested technique could extract static features while it is not affected from the changes of syllable lengths, it is effectively useful for korean numeric recognition system. Without decreasing classification rates, we can save the time and memory size for computation using KLT. The proposed feature extraction technique extracts same size of features form the tow same parts, front and end of a syllable. This technique makes frames, where features are extracted, using unique size of windows. It could be applied for continuous speech recognition that was not easy for the normal neural network recognition system.