Abstract
This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.
Highlights
Information fusion [1] refers to the process in which relevant information is searched and extracted from multiple distributed heterogeneous network resources and converted into a unified knowledge mode
Evaluation system aims at evaluating the satisfaction degree to problem solving plan, which is, evaluating the values of the knowledge paths contained in knowledge structure tree (KST)
Wj is weight; V(kpi(sgj)) is the contribution value provided by the j sub-target in the i knowledge path in achieving the total target; V(kpi(sgj) = f(tsgj, lsgj, nsgj ), f(tsgj, lsgj, nsgj ) are the evaluation functions related to the memory time, the layer number in KST, and the subnodes number of sub-target j
Summary
Information fusion [1] refers to the process in which relevant information is searched and extracted from multiple distributed heterogeneous network resources and converted into a unified knowledge mode. 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 regarded as that human brain comprehensively treats information. 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. Most of existing intelligent algorithms are proposed based on natural evolution rule, animal collective intelligence, and life system mechanism They fail to make good use of the background factors of problems and the knowledge produced in the process of solving problems.
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