In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter.

In this paper, Particle Swarm Optimization (PSO) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, Newthyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, PSO achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.

Markov models have been widely utilized for modeling user web navigation behavior. In this paper, we propose a novel adaptive weighting hybrid-order Markov model-HFTMM for Web pre-fetching based on optimizing HTMM (hybrid-order tree-like Markov model). The model can minimize the number of nodes in HTMM and improve the prediction accuracy, which are two significant sources of overhead for web pre-fetching. The experimental results show that HFTMM excels HTMM in better predicting performance with fewer nodes.

Multi-sensor image fusion is a challenging research field, which is a issue to be further investigated and studied. Self-Generating Neural Networks (SGNNs) are self-organization neural network, whose network structures and parameters need not to be set by users, and its learning process needs no iteration. An approach of image fusion using a SGNN is proposed in this paper. The approach consists of pre-processing of the images, clustering pixels using SGNN and fusing images using fussy logic algorithms. The approach has advantages of being wieldy to be used by users and having high computing efficiency, The experimental results demonstrate that the MSE (mean square error) of this approach decreases 30%-60% than those by Laplacian pyramid and discrete wavelet transform approaches.

This paper proposes Particle Swarm Optimization (PSO) algorithm to discover classification rules. The potential IF-THEN rules are encoded into real-valued particles that contain all types of attributes in data sets. Rule discovery task is formulized into an optimization problem with the objective to get the high accuracy, generalization performance, and comprehensibility, and then PSO algorithm is employed to resolveit. The advantage of the proposed approach is that it can be applied on both categorical data and continuous data. The experiments are conducted on two benchmark data sets: Zoo data set, in which all attributes are categorical, and Wine data set, in which all attributes except for the classification attribute are continuous. The results show that there is on average the small number of conditions per rule and a few rules per rule set, and also show that the rules have good performance of predictive accuracy and generalization ability.