An Intelligent Evaluation Method Based on Improved PSO-WNN for Cement Bond Quality

An intelligent evaluation method based on improved PSO-WNN(particle swarm optimization-wavelet neural network) for cement bond quality is presented because the pattern recognition accuracy of traditional relative amplitude methods are very low.Firstly,Lyapunov stability theory is used to discuss the convergence conditions of a single particle.Then, based on the results,a new strategy is introduced to improve the performance of the PSO algorithm.Secondly,the improved PSO algorithm is used to optimize the parameters of the WNN.The Iris benchmark data set is used to test the proposed algorithm. The improved method is compared with classic algorithms like BP-WNN and basic PSO-WNN.Simulation results confirm that the new algorithm not only has the performance of rapid global convergence,but also the iterative number,error of the function approximation and the classification accuracy of the new classifier are highly improved.Finally,the trained IPSO-WNN is used to identify the acoustic cement bond quality logging data.Experimental results show that the recognition ability of cement bond is improved greatly by means of the new method and the new method is high efficient and practicable for evaluation of cement bond quality.