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The stability of neural networks is not only the most basic and important problem but also the foundation of some neural network's applications. In this paper, the stability of discrete delayed Hopfield neural networks is mainly investigated by constructing Lyapunov function and taking some inequality techniques into account. The sufficient conditions for discrete delayed Hopfield neural networks...
Stochastic resonance phenomenon in a biological sensory system has been studied through signal detection theories and psychophysical experiments. In this paper, a sensory system is considered as a threshold detector including a receiver and a classifier. In comparison with conventional models regarding the receiver of sensory system as a linear or single non-linear system, a summing network is constructed...
The stock market is a typically complex and self-interacting system that in many aspects it shows the characteristic of self-organized criticality (SOC). In the present work we mainly investigate the SOC properties of individual companies. Price volatility can be served as an analogy of "avalanche" in the classical sand-pile model and different volatility statistics have different power...
A modified proximal support vector machine (MPSVM) is presented for the case of unbalanced data classification in many applications. The algorithm assigns different penalty coefficients to the positive and negative samples respectively by adding a new diagonal matrix in the primal optimization problem. And further the decision function is obtained. In addition, the real-coded immune clone algorithm...
In order to solving fault diagnosis of analog circuit with tolerances, noise, circuit nonlinearities and small sample sets, a novel multi-class classification algorithm which combined binary tree SVMs multi-classification based on self-organizing map nerve network (SOMNN) clustering roughly was proposed. The robustness characteristic of SOMNN based on the separability between pattern classes and support...
Hyper-sphere support vector machines are proposed for solving multi-class classification problem. How to correctly classify the intersections of hyper-spheres is important for sphere structure support vector machines. Based on the analysis of such data samples, this paper presents a new simple classification rule which leads to a better generalization accuracy than the existing sub-hypersphere SVMs...
A improved method of feature extraction based on kernel maximum margin criterion (KMMC) is presented for face recognition in this paper. i.e. a simple algorithm of uncorrelated optimal discriminant vectors in kernel feature space is proposed for nonlinear feature extraction. The proposed method has more powerful capability to eliminate the statistical correlation between feature vectors and its mathematical...
An analyzed model of drillstem failure reason has been constructed based on SVM technology and clustering analysis theory in this paper, and the optimal kernel function and several appropriate factors also have been obtained by training the sample data. Using the drillstem failure data come from Daqing oilfield, according to the requirements of the model, the data have been pretreated and analyzed,...
Kernel functions (kernel) are key part and the hard issue of Support Vector Machines. We research the relation of kernel functions and nonlinear mappings and mapped spaces. A new kind of admissible Support Vector Machines kernel is presented. It is autocorrelation kernel. The theory proofs certify that autocorrelation functions are admissible Support Vector Machines kernel. Several experiments also...
Audio classification is very important in audio indexing, analysis and content-based video retrieval. In this paper, we have proposed a clip-based support vector machine (SVM) approach to classify audio signals into six classes, which are pure speech, music, silence, environmental sound, speech with music and speech with environmental sound. The classification results are then used to partition a...
In this paper, a new method of fault diagnosis based on K-L transform and support vector machine(SVM) is presented on the basis of statistical learning theory and the feature analysis of vibrating signal of ball bearing. The key to the fault bearings diagnosis is feature extracting and feature classifying. Multidimensional correlated variable is converted into low dimensional independent eigenvector...
The back-propagation neural network is used to pricing call warrants, and the input variables of network model are investigated. The market call warrants prices quoted on Shanghai Stock Exchange and Shenzhen Stock Exchange are used to train and simulate the network model. The results show that the performances of the proposed network model produce better call warrant prices than Black-Scholes, and...
In this paper, we present a novel face detection approach based on Adaboosted Relevance Vector Machine (RVM). The novelty of this paper comes from the construction of the kernel classifier with different kernel parameters. We use Fisher's criterion to choose a subset of Haar-like features. The proposed combination outperforms in generalization in comparison with Support Vector Machine (SVM) on imbalanced...
In this paper, a novel and concise method for the selection of candidate vectors (SCV) is proposed based on the structural information of two classes in the input space. First, the Euclidean distance of all samples to the boundary of the other classes is calculated. Then the relative distance is computed to reorder training samples ascendingly, and boundary samples will rank in front of others and...
In order to improve the accuracy to diagnose rate earlier stage gastric cancer with Fourier Transform Infrared Spectroscopy (FTIR), a novel method of extraction of FTIR feature using continuous wavelet transform (CWT) analysis and classification using the support vector machine (SVM) was developed. To the FTIR of gastric normal tissue, early carcinoma and advanced gastric carcinoma, 9 feature parameters...
Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic algorithms and simulated annealing algorithms have...
Distribution centers location involves many factors, as a complicated systems engineering. A right location can get better economic benefit and society contribution. There are so many factors which influence the decision-making of the distribution center. Meanwhile there are relations between the factors because it's difficulty for us to set up an index system which only has independent indexes. In...
In order to evaluate efficiency in university laboratory in a reasonable way, the index system for efficiency evaluation is established, and the efficiency class is separated into three classes-good, fair, and poor. To classify the efficiency of three classes, the evaluation model of multi-layer support vector machines (SVM) classifier is established. In order to verify the effectiveness of the method,...
Present methods of facial expression recognition usually designate an expression image as one kind of six facial basic expressions. However, a facial expression usually is a complex expression that consists of several basic expressions. This paper proposes a facial complex expression recognition algorithm based on fuzzy kernel clustering and support vector machines. This algorithm designs the binary...
Support vector machine is intrinsically a binary classifier providing no theoretically formulated procedure for multi-category classification. Several methods have been developed to extend it to multi-category problems. Combining strengths of them, an improved "labeled multi-category support vector machine" is proposed. The proposed method explicitly labels samples and performs multi-category...
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