Authors’ ContributionZheng-jie Huang,Yilin Zhao, Wei-yuan Luo, Jun You, Shui-wen Li, Wen-cheng Yi, and Sheng-yu Wang performed the experiments and analyzed the data, and Qi Luo and Jiang-hua Yan designed the research and wrote the paper. Zheng-jie Huang and Yilin Zhao contributed find protocol equally to this research.AcknowledgmentsThis work was funded by the Key Projects of Fujian Province Technology (Grant no. 2010D026), Medical Innovations Topic in Fujian Province (Grant no. 2012-CXB-29) and also supported by Projects of Xiamen Scientific and Technological Plan (Grant no. 3502Z20124018). This research was performed in Xiamen University, China.
Information fusion [1] refers to the process in which relevant information is searched and extracted from multiple distributed heterogeneous network resources and then converted into a unified knowledge mode.
It aims at constructing effective knowledge resources for solving the problems in certain field or generating new integrative knowledge object by conversing, integrating, combining, and so forth various information coming from distributed information resources. Common information fusion algorithms can be divided into two main categories, which are probability statistics method and artificial intelligence method. Probability statistics method includes Bayes, the transformation of Bays [2], and D-S evidence reasoning [3]. It has axiomatic basis and low computational complexity and is intuitive and easy to be understood, but it needs more prior information and its applicable condition is harsher; while in artificial intelligence method, information fusion is similarly regarded as that human brain comprehensively treats information.
In this method, artificial neural network [4], support vector machine [5], and genetic algorithm (GA) [6] account for approximately 85% of the whole information fusion algorithm. And the machine learning methods, that is, swarm intelligence, artificial immune, quantum genetic algorithm, and so forth, have been applied in information fusion. This method shows fewer requirements to the prior information of object and stronger self-fitness. Moreover, the fusion of the subjective and objective information in system can be realized using this method. Most of existing intelligent algorithms are proposed based on natural evolution rule, animal collective intelligence, and life system mechanism.
However, they fail to make good use of the background factors of problems and the knowledge produced in the process of solving problems. This situation limits the natural combination of intelligent algorithm and knowledge to some extent and the full play of the role of knowledge.The researches on information Cilengitide fusion currently show a developing trend of further combining with the cognitive system-based human natural intelligence.