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Speech classification is an important part of speech signal processing. It is significant to classify speech accurately and quickly in speech coding and speech synthesis. Because of the diversity and uncertainty of the speech signals, the traditional classification method is slow and not so accurate in the large-scale application of real speech classification. In order to improve the accuracy and...
In this paper, two modeling methods are studied for billet temperature prediction model based on the actual production data of the reheating furnace in Tangsteel 1700 line. The two methods are the neural network and the support vector machine. Two prediction models are built by the two methods respectively. And the comparative research is done via MATLAB simulation aiming at the two modeling methods...
This paper uses genetic algorithm to optimize the relevance vector machine algorithm to extract the characteristic vector of fault classification, and by contrasting with relevance vector machine, the support vector machine and BP neural network method, it is know that the relevance vector machine optimized by genetic algorithm (ga) can more accurately classify the fault type of conclusion.
As a new model of distributed computing, all kinds of distributed resources are virtualized to establish a shared resource pool through cloud computing. The target of cloud computing is to provide convenient and configurable resource for users with pay-per-usage charging model. Therefore, the reasonable and efficient mechanism for resource allocating is becoming a hot spot in research. According to...
Aircraft icing can seriously affect the safety of aircraft, and different types of aircraft icing have an effect on the aircraft safety to various extents. A SVM (Support Vector Machine) model for aircraft icing type prediction is presented to classify aircraft icing types. The input variables of icing type are analyzed, and then based on the analysis, the appropriate forecast methods are chosen and...
LPR (License Plate Recognition) is a foundation component of modern transportation management systems. It uses a set of computer image-processing technologies to identify vehicle by its license plate. Character recognition is the core of LPR, which is essentially a multi-classification problem. The challenge is how to recognize every character of the license plate accurately and rapidly in case of...
Crustal deformation time series is a significant information source during the researches on continental deformation. In order to simulate the low frequency linear components which reflect dynamic trend as well as the high-frequency non-linear components which reflect disturbance, a prediction model based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented. With optimization...
The following topics are dealt with: linear approximation; license plate recognition; color image segmentation; image quantization; wireless video transmission; congestion control; stochastic search; transmembrane helical segments; wavelet transform; semisupervised cluster algorithm; anomaly detection; data privacy; online market information processing; user behavior; particle swarm optimization;...
The following topics are dealt with: data hiding techniques; wavelet transform; management information system; image sequence compressing algorithm; trusted software; case-based reasoning system; education information platform; UML; multiobjective optimization; information retrieval; web mining; CUDA architecture; fuzzy association rules; BP neural network; network security detection method; adaptive...
This paper describes the reliability and validation of prediction models of LAN/WLAN integration network. An improved PSO algorithm is used to optimize the weight of BP neural network. Support vector machine (SVM) is used in network reliability prediction. The LAN/WLAN integration network reliability prediction models are established with three methods (BP neural network, improved BP neural network...
A predictive model of water-quality, which based on wavelet transform and support vector machine, is proposed. This model uses wavelet transform to get water time sequence variations in different scale, and optimizes three parameters of Regression Support Vector Machine with improved Particle Swarm Optimization algorithm, to improve the accuracy of prediction model. This model is used to take one-step...
Support Vector Machine (SVM) is a very popular arithmetic, based on SVM, developed a paper defects recognition system. In the stage of paper defects image segmentation, proposed a algorithm based on the SVM, While in the stage of paper defects feature extraction, applied a multi-class SVM to classify the paper defects. Experimental results show that the proposed system yields faster recognition speed...
Slope stability is always a very complex issue in engineering. Base on the theoretical analysis of BP neural network and support vector machine (SVM), some major factors which influence the slope stability are selected in soil slope and the slope samples are trained and identified. The identification rates of BP neural network and SVM both achieved 100%. In identification precision and elapsed time,...
In order to represent 3D spatial entity effectively in geological engineering, a new method of geological information forecast and 3D reconstruction is put forward based on support vector machine (SVM). Firstly, for the given geological drill hole data, SVM is adopted to forecast ore grade of information unknown areas within the geological sections and then geological layered data is obtained. Secondly,...
The synergy effect's benefit is widely accepted. The object of this paper is to investigate whether a hybrid approach combining different stock prediction approaches together can dramatically outperform the single approach and compare the performance of different hybrid approaches. The hybrid model includes three well-researched algorithms: back propagation neural network (BPNN), adaptive network-based...
In the paper, support vector machine is presented to detection for vehicle' s overlap, which has stronger generalization ability than the algorithm based on the empirical risk, such as artificial neural network. In the process of detection for vehicle's overlap, principal component analysis is used to extract the features and reduce the dimension of features. Then, detection model for vehicle's overlap...
Blank holder force(BHF) forecasting is an important research direction in sheet metal forming. Support vector machine(SVM) is a novel learning machine based on the principle of structural risk minimization, which can solve the shortcoming of artificial neural networks, such as local optimization solution, lack generalization. In the study, SVM is presented to blank holder force forecasting. Cupshell...
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...
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