By Prateek Agrawal, Anupam Shukla, Ritu Tiwari (auth.), Wen Yu, Edgar N. Sanchez (eds.)
This ebook constitutes the court cases of the second one foreign Workshop on complicated Computational Intelligence (IWACI2009), with a sequel of IWACI 2008 effectively held in Macao, China. IWACI2009 supplied a high-level foreign discussion board for scientists, engineers, and educators to give cutting-edge study in computational intelligence and similar fields.
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All the experiments are based on the software LibSVM . For each dataset , 80 percent of the data is used for training and the rest is used for testing. The prediction performance is evaluated by Mean Square Error (MSE). Application Research of Local Support Vector Machines in Condition Trend Prediction Fig. 2. On mpg dataset, the predict-error and SVs as a function of MSE = 39 ε 1 n ( yi − yi′) 2 ∑ n i =1 (9) The results of the experiment are presented in Tabel 1. It can be clearly seen that the DD method is very competitive with the usual grid search method in terms of MSE and cost much less time, which is because DD method optimizes parameters C and λ without experiencing training process, compared with GS method 5 × 441 training times.
Yan and Y. Zhu parameters as user-defined inputs, or use grid search method based on k -folds cross validation to optimize these parameters. The former requires the user have a priori knowledge to the faced problem or user expertise. Obviously, this approach is not appropriate for non-expert users. The later is very time-consuming and not meets the time requirement of industry. In this paper, the optimization of parameters of SVMs is studied. and Local SVMs, combining the local algorithm and SVMs, and getting better performance than traditional SVMs in practice [5,6], is used to forecast the condition trend of reactor coolant pump.
If for a given set of initial phases [ϕ1 (0), ϕ2 (0), . . , ϕN (0)], there exists a t0 ≥ 0 such that the phase variables of arbitrary oscillators i and j satisfy ϕi (t) = ϕj (t), for all t ≥ t0 , (4) then we say that for this set of initial phases, the pulse-coupled oscillator network can achieve complete synchronization. Definition 2. If for a given set of initial phases [ϕ1 (0), ϕ2 (0), . . , ϕN (0)], the phase variables of arbitrary oscillators i and j satisfy lim d(ϕi (t), ϕj (t)) = 0, t→+∞ (5) where d(·, ·) is defined by d(θ1 , θ2 ) = min | θ1 − θ2 |, θ1 + 1 − θ2 , θ2 + 1 − θ1 , (6) then we say that for this set of initial phases, the pulse-coupled oscillator network can achieve asymptotic synchronization.