Some results referred to in Table 2. Table 2 The experiment results of ontology mapping. Taking N = 1, 3, or 5, the precision ratio in terms of our gradient computation based ontology mapping algorithm is higher than the precision ratio Nilotinib molecular weight determined by algorithms

proposed in [12, 13, 17]. Particularly, as N increases, the precision ratios in view of our algorithm are increasing apparently. Therefore, the gradient learning based ontology Algorithm 4 described in our paper is superior to the method proposed by [12, 13, 17]. 6. Conclusions As a data structural representation and storage model, ontology has been widely used in various fields and proved to have a high efficiency. The core of ontology algorithm is to get the similarity measure between vertices on ontology graph. One learning trick is mapping each vertex to a real number, and the similarity is judged by the difference between the real number which the vertices correspond to. In this paper, we raise a gradient learning model for ontology application in multidividing setting. The sample error and approximation properties are given in our paper. These results support the gradient computation based ontology algorithm

from the theoretical point of view. The new technology contributes to the state of the art for applications and the result achieved in our paper illustrates the promising application prospects for multidividing ontology algorithm. Acknowledgments This work was supported in part by the Key Laboratory of Educational Informatization for Nationalities, Ministry of Education, the National Natural Science Foundation of China (60903131), the College Natural Science Foundation of Jiangsu Province in China (10KJD520002), and the Ph.D. initial funding of the first

author. The authors are grateful to the anonymous referee for careful checking of the details and for helpful comments that improved this paper. Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.
Neural network (NN) is an interdiscipline, and it involves many subjects, such as computer, mathematics, neural, and brain. It is based on the intelligent computation of the computer network imitating biological neural network, which is good at dealing AV-951 with nonlinear problems and massive calculation. Neural network has the history of more than 70 years and hundreds of neural network models have been proposed, and different network models have their own superiority in dealing with different problems. Radial basis function (RBF) neural network is a three-layer feed-forward network with a single hidden layer; it can approach any continuous function with arbitrary precision, and it has some excellent characteristics, such as structure-adaptive-determination, independent of the initial value of output.