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The optimal estimation for wireless networked systems is studied, where observation is encoded separately and sent over wireless channel, thus the packet loss probability occurs. Multiplicative noises is considered in the system model, and the optimal estimator is derived in mean square sense, and some properties of the estimation error covariance matrix are proposed.
This paper presents a new classification algorithm on traffic state of expressway which integrates the ensemble learning and fuzzy system, which consists of two fuzzy classifiers and a speed-based classifier. The fuzzy rules of two fuzzy classifiers are developed based on expert knowledge and how to optimize the parameters in fuzzy classifiers is given. While the outputs of individual classifier are...
Nanomanipulation and nanoassembly using atom force microscopy (AFM) is a potential and promising technology for nanomanufacturing. Precise position of the tip of AFM is important to increase the accuracy and efficiency on fabricate complex nanostructures. However at the nano-scale, it is difficult to acquire the tip position expressed by the coordinate in real time due to PZT nonlinearity and thermal...
This paper studies and compares three nonlinear observers (Nonlinear Lyapunov Observer (NLO), Lipschitz Observer (LIO) and Partial Lipschitz Observer (PLIO)) applied to nonlinear model of the DC servo motor. The considered criteria of computations for white noise is the amplitude of the residual and the estimated shape of residual and error probability density functions (PDF) which is estimated by...
A novel geometry based matching approach is proposed for images with multi objects. In the proposed method, shape and topology ordered graphic is developed to model both shape and structural information for images. Vertexes are used to represent shape attributes, while edges are employed to define structural attributes between objects. The similarity between images is measured with two consecutive...
This paper deals with the output feedback stabilization problem for the stochastic nonholonomic systems with unknown stochastic disturbances. The objective is to design the almost global asymptotical output feedback controllers in probability for the systems by using discontinuous control. The switching control strategy based on the output measurement of the first subsystem is employed to achieve...
The delay-probability-distribution-dependent robust stability problem for a class of uncertain stochastic system with time-varying delay is investigated. The information of probability distribution of the time delay is considered and transformed into parameter matrices of the transferred stochastic model. Based on the Lyapunov--Krasovskii functional and stochastic stability theory , a delay-probability-distribution-dependent...
With the study and analysis on intelligent fault diagnosis for inverting circuit, an improved diagnosis method combined BP neuron network and D-S evidence theory was proposed. Each measuring point was extracted by BP neural network to obtain the local diagnosis, which is adopted to design the belief function of D-S evidence theory. Multiple monitoring points' information is fused to receive the comprehensive...
The article introduces the method of probabilistic neural network (PNN) and its classifying principle. It constructs a PNN structure for identified three patterns samples. The PNN structure is used to separate 106 listed companies of our country in 2000 into three groups. The simulations show that, the classification accuracy rate of PNN to the training samples is very high which is up to 100%, but...
An update dynamic programming algorithm is presented in allusion to the features of the moving infrared small dim target. The main idea of this algorithm is that the predicting states between frames and a low threshold are applied to reduce the computation. The background noise is suppressed by the image pre-processing. The probability of detecting and false alarm are used to determine the threshold...
Aiming at the disadvantage (premature convergence) of the Ant Colony Optimization (ACO), a mechanism called global random-proportional rule (GRP) is presented. The mechanism adjusts the transition probability proportionally and facilitates the exploration by increasing the probability of selecting solution components with low pheromone trail. It can avoid premature convergence of ACO and exploit more...
A method for attributes reduction in inconsistent decision tables is proposed in this paper. The discernible information in inconsistent decision tables is described with discernible vector array. The attribute reduction tree will generate based on the probability of the attributes which discern two objects. The classification of reduction table is same as that of the initial table.
Real-coded genetic algorithms (RCGAs) have been effectively used to solve constrained optimization problems (COPs). However, the crossover operators do not have mechanisms to handle constraints and there is no guarantee that if the parents satisfy some constraints the offspring will satisfy them as well. Degree preserving based crossover operators are proposed to increase the probability of constructing...
Binary ant colony algorithm has good performance in the function optimization problem. However, the drawbacks that easy to fall into the local optimization still exist. Through the analysis of “best-so-far” pheromone update rule, we get the lower probability bound under this update rule. Then binary ant colony algorithm with Balanced search bias is proposed. Experiment results have shown that the...
In an open electricity market, generation companies (GENCO) have to optimally bid to gain more profits with incomplete information of other competing generators. In this structure, market participants must develop their bids in order to maximize their profits. Building optimal bidding strategies for GENCO could need to evaluate some market parameters such as forecasting market-clearing price (MCP),...
Estimation of distribution algorithm (EDA) is a new evolutionary computation method based on probabilistic theory. EDA can select optimal individuals through estimating probability distribution function of a population. The capture problem among multi software robots can be solved by EDA. The capture problem involves that some pursuers pursue several evaders through part of trajectory. The trajectory...
This paper proposes an anisotropic probabilistic neural network image interpolation (APNNI2). The method used anisotropic Gaussian kernel to improve probabilistic neural network image interpolation (PNNI2) which caused blurring edge. The method is better for sharpness enhancement at edge region. We interpolate slanted-edge image to observe blurring and blocking effect. Finally, we report the performance...
k-means clustering has been widely applied in the field of Machine Learning and Pattern Recognition. This paper discussed the randomized algorithm of its sub problem which requires that each divided subset size has to be at least some given value. First a sample set was drawn at random from the given point, which contains some number of points of each optimal subset with high probability. Based on...
Based on a set of input - output data, we first build a joint probability density function of certain two-dimensional random variables utilizing fuzzy inference rules, then propose its marginal probability density function and numerical characteristics. Finally, it is pointed out that the fuzzy system constructed with center of gravity defuzzifier is a regression function in the sense of probability...
This paper constructs a stochastic fuzzy controller to realize self-balancing control of two-wheeled robot. The rule of fuzzy controller is stochastic, which is automatically generated by an OCPFA learning system and optimized online. The OCPFA learning system is in fact a Probabilistic Finite Automata (PFA) which based on Skinner Operant Conditioning (Skinner OC), and it is composed by a bionic reorientation...
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