Research On RBF Neural Network Algorithm And Its Application In High Dimensional Data Preprocessing

Nowadays,human society has entered the big data era,and most of the data are characterized by high dimensionality,large scale and complex structure.In the study of big data,many data such as media data,remote sensing data,biomedical data,social network data and financial data are high dimensional data,especially in the human production and living,an containing high dimensional data expensive multi-objective problem with no analytic model or high cost of an candidate solutions evaluation,and its simulation must cause dimensional disasters,and its simulation must cause dimensional disasters.Therefore,it is urgent to find a suitable method to deal with high dimensional data.Neural network is a distributed information processing system based on simulating the structure and function of brain,In the face of high-dimensional nonlinear multi-objective optimization problems,neural networks have great advantages over other dimensionality reduction methods,which are due to their highly nonlinear,complex structure,self-learning and self-adaptive characteristics.Radial basis function(RBF)neural network is a novel and effective feedforward neural network,which has strong nonlinear mapping ability,can approximate a nonlinear function globally with arbitrary accuracy,and has a fast learning speed.Using RBF neural network to reduce the dimensionality of high dimensional data not only has sufficient theoretical basis,but also has more advantages.This paper focuses on the dimensionality reduction method based on data-driven feature selection RBF neural network,and applies it to classification and Pareto dominance prediction.In order to improve the learning efficiency of RBF neural network,this paper firstly studies the improvement of RBF neural network Algorithm.By adjusting the learning rate and momentum factor of RBF neural network adaptively,the convergence rate of RBF neural network is accelerated.At the same time,the initial values of three parameters of RBF neural network are optimized by genetic algorithm,and a genetic adaptive RBF neural network algorithm is proposed.The improved algorithm is applied to fault diagnosis and the classification experiments of UCI data sets respectively,and the effectiveness and superiority of the improved RBF neural network algorithm is verified.Aiming at the no analytic model high dimensional multi-objective problem,this paper proposes a feature method combining the maximum information coefficientwith the maximum correlation minimum redundancy,and then using genetic adaptive RBF neural network algorithm select a low dimensional feature subset in high dimensional feature space,so as to realize dimension reduction of high dimensional feature space.Through the classification experiment on the UCI data set,it is proved that the dimensionality reduction algorithm can greatly reduce the calculation cost on the premise of ensuring better classification accuracy.In order to reduce the dimension disaster of high-dimensional multi-objective optimization problems,the feature selection algorithm of genetic adaptive RBF neural network based on maximum redundancy and minimum correlation was applied to the dimension-reduction preprocessing of decision space,then predict the Pareto dominance and embed the prediction algorithm to MOEAS.By comparing with the experimental results of NSGA-II,the results show that the feature selection algorithm of the genetic adaptive RBF neural network proposed in this paper greatly reduces the calculation cost and avoids the dimension disaster on the premise of obtaining an acceptable Pareto optimal solution.