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In robot soccer simulation team, how to select a proper behavior for a player among shoot, dribbling and passing is a key issue. This paper proposes a more flexible behavior selecting strategy. In the strategy, the fuzzy-algorithm is used to deal with behavior selecting issue. Because the environment of the robot soccer is complicated, with this algorithm it's not necessary to build a precise mathematic model about the environment. Simultaneously, Q-learning is used to modify the fuzzy rules. The experimental results show that this algorithm is more efficient and robust which can improve the success rate of robot player in shoot, passing and dribbling.

Because of a wide range of Industrial Ethernet and the great difference between them, it is a difficult problem to select which one is proper when designing an automation system. The huge advantages of Industrial Ethernet are discussed, so are the protocol model and technologic characteristics. The usage patterns and the usage percentages of industrial Ethernet are analyzed. Then the performance of industrial Ethernet is compared and evaluated. Especially, this paper presents that after consideration for some factors, such as development expense and complexity, protocol compatibility, market share and etc, the suitable industrial Ethernet protocol can be selected according to the performance demand of the automation systems.

The characteristics of small sample, stochastic growth and nonlinear wave are often combined with long-medium power load forecast series; SVM model could reflect the relationship between growing characteristics and nonlinear characteristics to the series effectively and make fitting calculation, on the other hand, Markov could well reflect randomness that produced by the system involve with many complex factors. Through establishment of a forecast model based on SVM algorithm, the series of historical load variables is rolling forecasted; an improved Markov error correction algorithm is introduced to modify the values forecasted by SVM, in order to make the increase of total forecasting precision to a maximum extent, a transfer matrix that make the forecast values to high stability and high accuracy is obtained. It is proved that the presented forecast method is superior obviously to traditional methods through empirical study, and it can be used generally.

A novel dynamic clustering algorithm based on artificial immune network is proposed in this paper. Firstly artificial immune network can get memory polls, which effectively represent the characteristics of fault samples, using the ability of immune memory and learning. Then genetic algorithm is used to dynamically optimize and select the best memory cells as initial clustering centers of kernel-based possibility clustering algorithm. A lot of fault samples are analyzed by this algorithm, and the results are compared with those obtained by BPNN. The results indicate that the samples can effectively be classified through the algorithm and precision of fault diagnosis can be improved.