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Time-delay is a widespread phenomenon of control system. Time-delay characteristics have a serious impact on system stability and performance. It is valuable to study iterative learning control of time-delay system. In this paper, we study the effects of multiple time delay on the convergence of nonlinear time-delay systems applying ??-norm and a set of inequalities. It will be shown that time-delay...
The traditional searching scheme of independent component analysis (ICA) is based on gradient algorithm. And a learning step size is required beforehand. It couldn't resolve the problem of convergence. To overcome the drawback, an improved particle swarm optimization (PSO) is applied to ICA algorithm. Firstly, the dynamic inertia weight which is based on evolution speed and aggregation degree is introduced...
In this paper, we provide the degree reduction algorithm for negative degree Bezier curves. By the uniform parameterization of control polygons and the definition of discrete derivatives of piecewise linear functions, we prove that the degree reduced control polygons and their discrete derivatives converge in the limit to the original negative degree Bezier curve and its continuous derivatives.
An improved functional link neural network was proposed for the identification of the dynamic system. In the improved method, the partial derivatives of the network outputs w.r.t its weights were re-deduced, and the more accurate evaluations of the derivatives were obtained. As a result, a novel recursive algorithm was developed to update the weights of the FLNN and a faster learning could be expected...
In this paper a global optimization algorithm is proposed for solving minimax linear fractional programming problem (P). By utilizing equivalent problem ( Q ) and linearization technique, the relaxation linear programming (RLP) about the (Q) is established. The proposed branch and bound algorithm is convergent to the global minimum of (Q) through the successive refinement linear relaxation of the...
Learning Bayesian networks can be examined as the combination of parameter learning and structure learning. Parameter learning is estimation of the conditional probabilities (dependencies) in the network. Structural learning is the estimation of the topology (links) of the network. The structure of the network can be known or unknown, and the variables can be expressed as complete and incomplete data...
In this paper an improved genetic algorithm is proposed to solve optimal problems applying fixed-point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on an J1 subdivision of searching space and generates the integer labels at the vertices, and then crossover operators and mutation operators relying on the integer labels are designed. In this case, whether every individual...
Based on martingale theory in Banach space, using the limit theorem of the B-valued martingale difference sequence, a strong limit theorem for arbitrary sequence series of B-valued random variables is obtained. In addition, according to Radon-Nikodym property of real number space, the main result of this paper contains some well known conclusions in the real number space.
Particle swarm optimization (PSO) algorithm is widely used in function optimization, but the performance of PSO algorithm is restricted as frequently occurrences of the premature. Introduce immune operator to PSO algorithm can be to avoid premature and improve the performance of algorithm. For the four benchmark functions, the results show that the immune operator improve performance of algorithm...
A modified dynamic differential evolution was proposed for discrete optimization. Based on the new framework of dynamic differential evolution, two additional operators were used to extend the dynamic differential evolution to the field of discrete optimization. The first operator was the mapping operator, which could map the continuous value into zero or one. The other new operator was the boundary...
The knapsack problem is formulated as a discrete optimization problem. In this paper, a solution strategy based on an improved binary PSO is presented. It applies new update functions and the strategy of disturbance to deals with the knapsack problem. Furthermore, a penalty function is suggested to change constrained problem into an unconstrained one. The example shows that this algorithm has a faster...
This paper studies how useful the standard 2-norm regularized SVM is in approximating the 1-norm SVM problem. To this end, we examine a general method that is based on iteratively re-weighting the features and solving a 2-norm optimization problem. The convergence rate of this method is unknown. Previous work indicates that it might require an excessive number of iterations. We study how well we can...
The service composition composing the existing Web services to form new, satisfying different user requirements and value-added composition service has become new application requirement and popular research. The optimization of services composition is a nonlinear multi-objective optimization problem which has been proven to be NP-complete. It is preponderant to settle the multi-objective optimization...
Truncation method has been proven to be effective for computing singular minimizers or singular minimizing sequences in variational problems involving the Lavrentiev phenomenon. But truncation area involving the singular set must be selected first when using the truncation method. In this paper three numerical methods for selecting truncation area are introduced, consequently which can detect singular...
Fuzzy set theory becomes more popular in recent years duo to its simplicity in modeling and effectiveness in control problems. In this paper, we introduce the concepts of countable dense subset of (0, 1) and countable nested sets. Based on the countable nested sets, we propose countable decomposition theorem and countable representation theorem for fuzzy set and use them to investigate fuzzy series...
In order to increase the diversity of immune algorithm when solving high-dimensional global optimization problems, a novel clonal selection algorithm with randomized clonal expansion strategy(RCSA) is proposed. The main characteristic of RCSA is clonal expansion. In addition, a novel performance evaluation criterion is constructed in this paper, by which the performance of different population-based...
In this paper, we design a ternary even symmetric 2n-point approximating subdivision scheme which generates smooth curves of high order. We illustrate the technique with a new 4-point ternary approximating subdivision scheme which is C4 and a new 6-point ternary approximating subdivision scheme which can achieve C7-continuity. The smoothness of the new schemes is proved using Laurent polynomials.
This paper deals with clustering for multi-view data, i.e. objects described by several sets of variables or proximity matrices. Many important domains or applications such as information retrieval, biology, chemistry and marketing are concerned by this problematic. The aim of this data mining research field is to search for clustering patterns that perform a consensus between the patterns from different...
In this paper, we restudy the non-convex data factorization problems (regularized or not, unsupervised or supervised), where the optimization is confined in the nonnegative orthant, and provide a unified convergency provable solution based on multiplicative nonnegative update rules. This solution is general for optimization problems with block-wisely quadratic objective functions, and thus direct...
This paper is concerned with the distributed averaging problem over a given undirected graph. To enable every vertex to compute the average of the initial numbers sitting on the vertices of the graph, the policy is to pick an edge at random and update the values on its ending vertices based on some rules, but only in terms of the quantized data being exchanged between them. Our recent paper showed...
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