An intelligent algorithm of Support Vector Regression parameters optimization in soft measurements

The Support Vector Regression (SVR) parameters selection problem is considered in this report. The purpose of work is to create an intelligent algorithm for SVR parameters optimization and, thus, to improve the accuracy of modeling in soft measurements (on example of the rice leaves modeling and data processing). Initially, the program collects the statistical data of learning assemblies analysis results and evaluates the values of SVR parameters, after then it uses developed by the authors improved fruit fly optimization algorithm to get more accurate values of SVR parameters. During the experiments, the obtained results were compared with the results of work of classical Fruit Fly Optimization Algorithm (FOA). It was found, that the authors' algorithm works more effective in convergence speed and accuracy. Thus, it provides the better solution of SVR optimal parameters selection problem.