Advances in Neural Networks – ISNN 2013
10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I
Chapter and Conference Paper
For the convenience of computer simulation, the discrete-time systems in practice are often considered. In this paper, Discrete-time cellular neural networks (DTCNNs) are formulated and studied in a regime whe...
Chapter and Conference Paper
In this paper, a neural network model and its associate learning rule are developed for sequential blind extraction in the case that the number of observable mixed signals is less than the one of sources. This...
Chapter and Conference Paper
This paper presents a new method for automatic gait recognition using independent component analysis (ICA). Firstly, a simple background subtraction algorithm is introduced to segment the moving figures accura...
Chapter and Conference Paper
A neural network based interactive physical programming approach is proposed in this paper. The approximate model of Pareto surface at a given Pareto design is developed based on neural networks, and a map fro...
Chapter and Conference Paper
In this paper, discrete-time cellular neural networks with one-dimensional space invariant are designed to associative memories. The obtained results enable both heteroassociative and autoassociative memories ...
Chapter and Conference Paper
Through the analysis of function approximation with wavelet transformation, an adaptive wavelet neural network is introduced in the paper, which is applied in data compression of fault data in power system. In...
Chapter and Conference Paper
A class of uncertain neural networks with time-delays on states and inputs is studied. The theoretical analysis herein guarantees that the neural networks are robust stable. In addition, a state feedback contr...
Chapter and Conference Paper
The paper is an extension of previous work on spatial-varying Gaussian mixture and Markov random field (SVGM-MRF) from 2D to 3D to segment the MR brain volume with the presence of noise and inhomogeneity. The ...
Chapter and Conference Paper
In this paper, global exponential stability in Lagrange sense is further studied for continuous recurrent neural network with three different activation functions. According to the parameters of the system its...
Chapter and Conference Paper
The implementation of multi-valued logic with a three layers forward neural network is proposed. The hidden layer is constituted by bi-threshold neurons compared with traditional simple threshold neurons. Acco...
Chapter and Conference Paper
To achieve quantum error-correction codes with good parameters, the recursive constructions of Hadamard matrices with even length are proposed with special characters. The generators of the stabilizer of the d...
Chapter and Conference Paper
Entropy changes with the variation of the system status. It has been widely used as a standard for the determination of system status, quantity of system complexity and system classification. Based on the sing...
Chapter and Conference Paper
In this paper we discuss the issue relevant to the parallel implementation of wavelet and wavelet packet on the single PC with multi-core system. The massive data compression has to manage large size of data w...
Chapter and Conference Paper
In order to fully mine the characteristics of load data and improve the accuracy of power system load forecasting, a load forecasting model based on Ensemble Empirical Mode Decomposition (EEMD) and Artificial ...
Chapter and Conference Paper
The current optimization algorithms for feature selection are mostly based on binary coded swarm intelligence algorithms. A novel real coded optimization algorithm which using the weighted distance metric is p...
Chapter and Conference Paper
To improve the precision of classification and recognition of transient power quality disturbances, a new algorithm based on spectral kurtosis (SK) and neural network is proposed. In the proposed algorithm, Mo...
Chapter and Conference Paper
In this paper, we propose a novel dimension reduction method based on canonical correlation analysis, called discriminative locality preserving canonical correlation analysis (DLPCCA) method. In particular, we...
Book and Conference Proceedings
10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I
Book and Conference Proceedings
10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II
Chapter and Conference Paper
Support Vector Data Description(SVDD) is an important method to solve data description or one-class classification problem. In original data description problem, only positive examples are provided in training...