Abstract:

Aiming at the problems in the evaluation process of the scientific research abilities due to its multiple factors and high non linear characteristics, as well as the subjective assessment in classic models, which leads to low accuracy, we propose an evaluation model of the collaborative ability of scientific research based on the intelligent water drops (IWD) algorithm and the rough set block neural network (RBNN). We first introduce the IWD algorithm, and design a local spatial auto scaling algorithm (LSSA) to solve the problem of fixed beside domain search range of the traditional IWD algorithm that is not conducive to improve search efficiency. The LSSA can automatically adjust the next search space size according to the best individual of current population, thus improving the evolutionary efficiency of algorithms. Based on the rough set theory, the data of scientific research ability of universities is preprocessed, which can simplify data calculation. Finally, the parameters of the block neural network and the rough set are encoded, and the model of scientific research abilities is evaluated. Simulation results show that the model has high accuracy and fast computational efficiency.  

Key words: intelligent water drop (IWD);block neural network;rough set;efficient scientific research ability;cooperative computation