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Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks (BTNs) evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper.

We abstract the bus transport networks (BTNs) to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm (PKM agglomerative algorithm), we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs', we get obvious community property.

On the basis of investigating the statistical data of bus transport networks of three big cities in China, we propose that each bus route is a clique (maximal complete subgraph) and a bus transport network (BTN) consists of a lot of cliques, which intensively connect and overlap with each other. We study the network properties, which include the degree distribution, multiple edges' overlapping time distribution, distribution of the overlap size between any two overlapping cliques, distribution of the number of cliques that a node belongs to. Naturally, the cliques also constitute a network, with the overlapping nodes being their multiple links. We also research its network properties such as degree distribution, clustering, average path length, and so on. We propose that a BTN has the properties of random clique increment and random overlapping clique, at the same time, a BTN is a small-world network with highly clique-clustered and highly clique-overlapped. Finally, we introduce a BTN evolution model, whose simulation results agree well with the statistical laws that emerge in real BTNs.

A method is proposed for the estimation of system model uncertainty by parameter sets. The uncertainty can be parametric or nonparametric, or both. And the estimation is guaranteed to contain the parametric set of the true plant without a priori knowledge of the model uncertainty. A robust control is then established with the parameter set obtained.

This paper first reviews wavelet transforms and multiresolution analysis. Then, the structures and learning algorithms of orthogonal wavelet networks(OWN) are discussed. The orthogonal wavelets are used as activation functions of the hide nodes. Finally, nonlinear dynamic system identification using the orthogonal wavelet networks is investigated. Computer simulations verify the theoretical result.

The problem of Hinfin analysis and synthesis of linear uncertain systems is addressed via parameter-dependent Lyapunov functions. The polyqudratic Hinfin stability as well as the affine quadratic Hinfin stability is defined, and the corresponding sufficient conditions of LMI form are derived based on a descriptor system approach. By refraining the slack matrices, Hinfin state feedback controllers could also be constructed by LMI constraints. The effectiveness is demonstrated by a numerical example.

For the strong gammak-gammacl Hinfin stabilization problem, a new bounded real lemma is provided by adding two slack variables, which provides extra freedom to include more control constraints in handling robust control problems. With the extended bounded real lemma, a new sufficient condition is proposed for n-th order strong gammak-gammacl Hinfin stabilization. Furthermore, the Hinfin stable stabilization and strong stabilization problems can also be solved by relaxing certain constraints accordingly. Despite the BMI constraints, the path-following and alternative iteration methods are adopted for the controller design. The effectiveness of the algorithms is indicated by a numerical example.

Orthogonal polynomials and functions have been used extensively by many researchers to solve control problems such as those of optimal control, system analysis and identification. In this paper, a general orthogonal polynomial operator (GOPO) is proposed and its properties are studied. The new operator is linear, bounded and continuous from the Hilbert space L2ρ[O,T] to a Banach space Mm. A class of three-dimensional matrix is introduced and a set of operational matrixes are summarized to establish operational rules of GOPO. Ten rules similar to those of Laplace operator have been set up. by which a lot of problems in control field can be approximately solved under rigorous mean-square covergency. The new GOPO approach is not only more rigorous and more general than those existing GOPs methods, but also straightforward and convenient for computation. Its applications to systems analysis and identification of time-varying systems are discussed and illustrative examples are given to show the effectiveness of the new GOPO method.

For a lack of complex intersection effective control method, a novel single intersection intelligent control technique was presented. Signal lamp setting, phase division, pedestrian press-button response, signal lamp advance-off and late-on were discussed in detail. Being a function of traffic flow saturation degree the cycle time was optimized by one fuzzy neural network, and the variable splits were adjusted based on historical and real-time traffic information. The goal was to make the intersection expedite, vehicles stop rate shortest and average vehicle delay time less. However, the crossing-crosswalk safety of pedestrian had to be assured firstly. This control technique has been used successfully in the intersection of No. 9 road and No. 12 road in Xiasha economic technical development area Hangzhou, China, and the application results have shown to be very good

A variable structure control (VSC) scheme for linear MIMO systems based on support vector machine (SVM) is developed. By analyzing the characters of linear MIMO system, a VSC scheme based on Exponent Reaching Law is adopted to track desired trajectory. Then one input of the system is trained as the output of SVM, while sliding mode function, differences and other inputs of the system are trained as the inputs of SVM. So one VSC input of the black-box system could be obtained directly by trained SVM after other inputs of the system are selected manually, and recognition of system parameters is avoided. A linear MIMO system is used to prove the scheme, and simulation results show that this scheme has high identification precision and quick training speed.

The distributed uncorrelated optimal fusion algorithm is received by avoiding computing correlated estimation covariance based on the matrix operation. Compared with classical multisensor distributed suboptimal algorithm and optimal fusion algorithm, this algorithm can adapt to the system with more than three sensors and has the advantages of the count capacity because it has no use for saving and computing the middle variable. Simulations show the developed-algorithm has an excellent-estimation performance.

This paper uses large-scale systems decomposition-coordination principle, fuzzy theory and neural networks technique to solve the problem of real time coordinated control for urban trunk road. Firstly, a neural macroscopic dynamic model based on the Kashani model for urban trunk road is proposed. Secondly, a two-level coordinated fuzzy control method implemented by neural network is presented. Based on the traffic volume data measured from each intersection the coordinated layer is coordinated with the number vehicle between the adjacent intersections. The operating layer adjusts on-line signal cycle and splits of every intersection. The object of the control method is to unblock the traffic trunk road and to shorten the average vehicle delay time. The simulation shows the proposed method has better performance than the fixed time control method.

From the viewpoint of macroscopical dynamic characteristics of urban expressway traffic flow, a commonly used macroscopic dynamic deterministic traffic flow model for traffic control is analysed, the neural network model for the urban expressway traffic flow is established and the urban expressway multi-variable neural control strategy with both the on-ramp control and the road speeds control is presented. This control strategy is a servo system where both state and control can be followed. Finally as an example, Hangzhou urban expressway is simulated and the result is satisfied.

Many centralized fusion estimation algorithms assumed that measurement noises among sensors are uncorrelated, but it is not true when there exist the same unmodeled measurement noise source. In this paper, based on the matrix similarity transform, covariance matrix of correlated measurement noise is successfully parallel decomposed and the linear measurement models are transformed. Optimal centralized fusion estimation algorithm is presented. When measurement noises are uncorrelated, the results here are reduced to the standard optimal centralized fusion estimation algorithms.

Measurement fusion is an optimal data fusion algorithm. The covariance of measurement fusion is proved to be decided by a function of the measurement matrix and measurement noise covariance. The greater the function is, the less the covariance measurement fusion method can obtain. Therefore, the estimation accuracy increases when the function increase. The results of simulation agree with the theoretical results.