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Choosing the right counterpart can have a significant impact on negotiation success. Unfortunately, little research has studied in such negotiation counterpart decisions. The purpose of this study is to develop negotiation agents that can behave rationally so as to improve the final outcomes, these agents employ support vector machine empowered by genetic algorithm with the same strategy used before...
In the fault diagnosis based on support vector machine (SVM), SVM parameters are mostly selected artificially or obtained through experiment time after time, a certain and effective method has not been found. Aiming at this problem, a method optimizing the SVM parameters with Monkey-King genetic algorithm (MKSVM) is presented. In the built model the optimized parameters are used, and the superiority...
This research constructs the CSO+SVM model for data classification through integrating cat swam optimization into SVM classifier. There are two factors (i.e. feature selection and parameter determination) of classification problems will mainly discuss in this study. The objectives of feature selection are to reduce number of features and remove irrelevant, noisy and redundant data. Besides, the parameter...
The generalization error of support vector machine usually depends on its kernel parameters, but there is no analytic method to choose kernel parameters for SVM. In order to choose the kernel parameters for SVM, the simulated annealing algorithm and genetic algorithm are combined, which is called simulated annealing genetic algorithm (SA-GA), to choose the SVM kernel parameters. SA-GA makes use of...
Site selection of urban track traffic routes is significant to improve the core competitiveness of urban public transport system. In the paper, the combination method of support vector machine and genetic algorithm (GA-SVM) is applied to site selection of urban track traffic routes, in which genetic algorithm (GA) dynamically optimizes the parameters of SVM. Site selection of urban track traffic routes...
Intrusion detection is a critical component of secure information systems. Data Intrusion Detection Processing System often contains a lot of redundancy and noise features, bringing the system a large amount of computing resources, a long training time, a poor real-time, and a bad detection rate. For high dimensional data, feature selection can find the information-rich feature subset, thus enhance...
Hybrid Electric Vehicles (HEV) offer the ability to significantly reduce fuel consumptions and emission. Management of energy is one of essential elements in the implementation of hybrid electric vehicles. Engine and motor should satisfy the driver's demand in different driving environment. This paper defines a driving cycle sensitivity parameter, which is used to create different driving cycles to...
The increasing importance and complexity of STLF necessitates more accurate load forecast methods. A novel genetic algorithm (GA) based support vector machine (SVM) forecasting model with determinstic annealing (DA) clustering is presented in this paper. For NN forecasting, too many training data may lead to long training time and slow convergent speed. First deterministic annealing (DA)for load data...
In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at...
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method combines both support vector machine (SVM) and genetic algorithm approaches. First, twenty two features from electrocardiogram signal are extracted. These features are obtained semiautomatically from time-voltage of R, S, T, P, Q features of an Electro Cardiagram signals. Genetic algorithm...
Target recognition algorithm based on support vector machine of optimum parameters is put forward in this paper. Firstly, local surrounding-line integral bispectrum feature is extracted from the bispectrum of range profile of target. Secondly, parameter scope is obtained through experiment method, optimum parameters of support vector machine are gotten using genetic algorithm. Finally, support vector...
Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier,...
Accurate electricity price forecasting can provide crucial information for electricity market participants to make reasonable competing strategies. Support vector machine (SVM) is a novel algorithm based on statistical learning theory, which has greater generalization ability, and is superior to the empirical risk minimization principle as adopted by traditional neural networks. However, its generalization...
Walking stability is the main reason for leading to falls for people, especially for elders. But there are much more features related with walking so that we cannot understand which features are more important than others to contribute the walking stability. Almost all of researches focused on some specific features but didn't present any reasons for that. Therefore, the Dynamic Time Warping (DTW)...
The cutting tool condition monitoring technology is very important to the automated production, which is a small sample but very complicated system on account of the limited experiment data. In this paper, with feature extracted from the workpiece surface image, the tool wear predictive model is built based on SVM. The proposed method use Genetic Algorithm adjusts SVM kernel parameter. Experiment...
The fouling of heat exchanger is an unsolved difficult problem in all over the world. The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. The application of Support Vector Machine (SVM) based on Statistical Learning Theory to predict heat exchanger fouling was introduced, and the Genetic Algorithm (GA) was...
Supervised learning is well-known and widely applied in many domains including bioinformatics, cheminformatics and financial forecasting. However, the interference from irrelevant features may lead to the poor accuracy of classifiers. As a popular feature selection model, GA-SVM is desirable in many of those cases to filter out irrelevant features and improve the learning performance subsequently...
In order to analyze the property of the blended coal in the power plants boiler operation, the author structure a new kind of hybrid model for blended coal based on Genetic Algorithm (GA) and support vector machine (SVM).The results of testing 20 blended coals in power plant show that the proposed model is practical and feasible. The model can achieve higher classification accuracy.
As a new detecting landmine method, Ground Penetrating Radar (GPR) is introduced into the field of detecting buried landmine. In order to improve the detection accuracy, A approach based on the Support Vector Machine (SVMs) is presented in the paper. The Support Vector Machines (SVMs) has solved the inevitable partial minimum problem and overcome the disadvantage which the traditional neural network...
Support vector machine (SVM) is a novel and popular technique for pattern classification and regression estimation. In the training process of SVM it is of great importance to determine a few tuning parameters to ensure the good performance of SVM. However, the widely used optimization methods such as Particle Swarm Optimization and Genetic Algorithm have the disadvantages of low convergent speed...
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