• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.116-123
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• 2013

Currently, most automobiles have automatic transmission systems. The gear-shifting strategy used to generate shift patterns in transmission systems plays an important role in improving the performance of vehicles. However, conventional transmission systems have a fixed type of shift map, so it may not be enough to provide an efficient gear-shifting pattern to satisfy the demands of driver. In this study, we developed an intelligent strategy to handle these problems. This approach is based on a normalized radial basis function neural network, which can generate a flexible gear-shift pattern to satisfy the demands of drivers, including comfortable travel and fuel consumption. The method was verified through simulations.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.468-475
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• 2008

This paper presents a method for estimating the transient stability status of the power system using radial basis function(RBF) neural network with a fast hybrid training approach. A normalized transient energy margin(${\Delta}V_n$) has been obtained by the potential energy boundary surface(PEBS) method along with a time-domain simulation technique, and is used as an output of the RBF neural network. The RBF neural network is then trained to map the operating conditions of the power system to the ${\Delta}V_n$, which provides a measure of the transient stability of the power system. The proposed approach has been successfully applied to the 10-machine 39-bus New England test system, and the results are given.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.431-434
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• 2016

Currently, the radial basis function network (RBFN) and various other neural networks are employed to classify gases using chemical sensors arrays, and their performance is steadily improving. In particular, the identification performance of the RBFN algorithm is being improved by optimizing parameters such as the center, width, and weight, and improved algorithms such as the radial basis function network-stochastic gradient (RBFN-SG) and radial basis function network-normalized stochastic gradient (RBFN-NSG) have been announced. In this study, we optimized the number of centers, which is one of the parameters of the RBFN-NSG algorithm, and observed the change in the identification performance. For the experiment, repeated measurement data of 8 samples were used, and the elbow method was applied to determine the optimal number of centers for each sample of input data. The experiment was carried out in two cases(the only one center per sample and the optimal number of centers obtained by elbow method), and the experimental results were compared using the mean square error (MSE). From the results of the experiments, we observed that the case having an optimal number of centers, obtained using the elbow method, showed a better identification performance than that without any optimization.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.