He Zhenya

Extracting rules from fuzzy neural network by particle swarm optimisation

A four layer fuzzy neural network is presented to realise knowledge acquisition from input-output samples. The network parameters including the necessary membership functions of the input variables and the consequent parameters are tuned and identified using a modified particle swarm algorithm which uses each particles best current performance of its neighbours to replace the best previous one and uses a non accumulative rate of change to replace the accumulative one for accelerating search procedure. The trained network is then pruned so that the general rules can be extracted and explained. The experimental results have shown that the similar classification rules can be obtained in comparison to that of other fuzzy neural approaches.

A multiple objective optimization based echo state network tree and application to intrusion detection

Echo state network tree (ESNTree) is proposed in this paper. ESNTree changes the activation function of the hidden layer and modifies the initialization method of echo state network (ESN). If the number of input features is too large, genetic algorithm is used to extract better features. Thus the complexity of input feature space can be reduced. On the other hand, a divide-and-conquer method is used. Decision tree and ESN are combined to decrease the complexity of the classifier and make the classifier more comprehensible and more interpretable. Experiments show that ESNTree achieves better performance than neural network tree (NNTree). ESNTree has been applied to intrusion detection successfully.

Doubly selective fading channel estimation in MIMO OFDM systems

High data transmission rates and high mobility give rise to time- and frequency-selectivity in wireless communication channels. This paper investigated time and frequency doubly selective channel estimation using pilot tones among Multi-Input Multi-Output (MIMO) orthogonal frequency division multiplexing (OFDM). Firstly, a complex exponential basis expansion channel model (BECM) was introduced to represent doubly selective channel during an OFDM symbol period; then, based on BECM, an effective MIMO-OFDM doubly selective channel estimation method was presented; finally, the optimality in designing pilot tones parameters was done according to the minimization of channel estimation MSE, mainly including the number, the placement and the structure of pilot tones. Simulation results show that the proposed estimation method has good performance in doubly selective channel scenarios and confirms the theoretical analysis findings.High data transmission rates and high mobility give rise to time- and frequency-selectivity in wireless communication channels. This paper investigated time and frequency doubly selective channel estimation using pilot tones among Multi-Input Multi-Output (MIMO) orthogonal frequency division multiplexing (OFDM). Firstly, a complex exponential basis expansion channel model (BECM) was introduced to represent doubly selective channel during an OFDM symbol period; then, based on BECM, an effective MIMO-OFDM doubly selective channel estimation method was presented; finally, the optimality in designing pilot tones parameters was done according to the minimization of channel estimation MSE, mainly including the number, the placement and the structure of pilot tones. Simulation results show that the proposed estimation method has good performance in doubly selective channel scenarios and confirms the theoretical analysis findings.

A design method for complementary recursive fan filters

A very efficient method for designing two-dimensional (2-D) recursive fan filters is presented. The odd-ordered analog elliptical filter is used as a 1-D prototype to synthesize the complementary quadrant fan filters, as well as 90 degrees fan filters, by means of an index transformation. The algorithm results in a great reduction in the required multiplications and in the simplified realization structure composed of all-pass filters as compared with the published design procedures. The design of zero-phase fan filters is also discussed. Only N-1 and 2(N-1) multiplications are needed to implement non-zero- and zero-phase complementary fan filters, respectively, if an Nth-order 1-D prototype elliptical filter is adopted.<<ETX>>

Design of perfect reconstruction cosine-modulated QMF banks

The design problem of cosine-modulated QMF banks that satisfy the perfect-reconstruction (PR) property has been formulated as a quadratic constrained least-squares (QCLS) minimization problem in which all constrained matrices of the QCLS optimization problem are symmetrical and positive definite. In order to efficiently solve this QCLS optimization problem, we construct a cost function which is a convex function of our desired prototype filter coefficients. So, all its minimizers are global minimizers. Results of computer simulations are presented to support our derivatives and analyses.

Linear phase paraunitary cosine modulated filter bank design formula

We propose the quadratic-constrained formulas for the design of linear phase cosine modulated paraunitary filter banks. Using these formulae, we can, directly, optimize the prototype filter coefficients in a quadratic form. A design example is also given to demonstrate these formulae in this paper.

A neural network based fault fuzzy diagnostic system

A fault fuzzy diagnostic system (FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.

Topological Self-Similar Networks Introduced by Diffusion-Limited Aggregation Mechanism

We propose a model for growing fractal networks based on the mechanisms learned from the diffusion-limited aggregation (DLA) model in fractal geometries in the viewpoint of network. By studying the DLA network, our model introduces multiplicative growth, aging and geographical preferential attachment mechanisms, whereby featuring topological self-similar property and hierarchical modularity. According to the results of theoretical analysis and simulation, the degree distribution of the proposed model shows a mixed degree distribution (i.e., exponential and algebraic degree distribution) and the fractal dimension and clustering coefficient can be tuned by changing the values of parameters.

Multiscale Entropy under the Inverse Gaussian Distribution: Analytical Results

The multiscale entropy (MSE) reveals the intrinsic multiple scales in the complexity of physical and physiological signals, which are usually featured by heavy-tailed distributions. However, most research results are pure experimental search. Recently, Costa et al. have made the first attempt to present the theoretical basis of MSE, but it only supports the Gaussian distribution [Phys Rev. E 71 (2005) 021906]. We present the theoretical basis of MSE under the inverse Gaussian distribution, a typical model for physiological, physical and financial data sets. The analysis allows for uncorrelated inverse Gaussian process and 1/f noise with the multivariate inverse Gaussian distribution, and then provides a reliable foundation for the potential applications of MSE to explore complex physical and physical time series.

Stability analysis of nonlinear observer with application to chaos synchronization

In this paper, we first discuss the stability of linearized error dynamics of the nonlinear observer used for time-continuous driving chaos synchronization and give the criteria on it. Then we find by theoretical analysis and numerical experiments that the observer can still synchronize with the original system under time-discrete driving provided that some conditions are met. Finally we derive the asymptotical stability criterion of the nonlinear observer used for time-discrete driving chaos synchronization. Simulations illustrate the validity of the criterion.