Assessment of College Students' Innovation and Entrepreneurship Ability Based on Fish Swarm Algorithm BP Neural Network

Through the fast growth of society and the improvement of knowledge economy, developing the college student's innovation and entrepreneurship has significantly becomes a crucial process. The present assessment system is inappropriate for encountering the implementation requirements of colleges as well as universities. Thus, identifying and introducing a novel assessment method is most vital for validating a ability of innovation and entrepreneurship of college students. Thus, this research proposes the Fish Swarm Algorithm based Back Propagation Neural Network (FSA-BPNN) for the ability assessment of college student's innovation and entrepreneurship. Assuming that the BPNN is susceptible to to local convergence and parameterization issues, the FSA has optimized the network's parameters, enhanced a training effect of a network, and improved an application effect of optimized approach for developing innovation and entrepreneurship abilities. The experimental results demonstrates that the proposed FSA-BPNN approach attains the better expected output value of 0.891 as compared to the other existing approaches.