Prediction of coal and gas outburst intensity with Incorporate GeneticAlgorithm Based Back Propagation Neural Network(IGABP)

For the prediction of coal and gas outburst intensity, Incorporate Genetic Algorithm Based Back Propagation Neural Network(IGABP) is proposed to solve the limitations in the traditional GABP such as time consuming, optimal stop condition of GA pretreatment indeterminacy, independency and complex task of great importance etc. IGABP addresses some improvements in adaptive crossover and mutation probability to promote GA performance. And with the introduction of BP operator in the evolution of GA operations, the standard GA optimization is from random search to self guiding search and the convergence rate of GA is upgraded, as well as the determination ability of exact solution. With a simulation as a case study, it is found that the minimum error and standard error with IGABP are 0.012 and 0.227, respectively, compared with -0.126 and 1.529 by traditional GABP.     

Intelligent control of the grinding and classificationsystem based on fuzzy RBF neural network

Based on RBF neural network and fuzzy theory, an intelligent control method, which can effectively overcome disturbance resulting from grinding efficiency and cyclone’s inlet pressure, is proposed. This method that can make grinding concentration and overflow particle size well proportioned will allow us to improve flotation grade and increase yield, and therefore realize the optimization of grinding and classification process. The present method is of simple analysis, less time of network learning and training. And high learning precision is high. The simulations show that our approach can also be applied to the control systems that are difficult to build accurate math model.