Forecasting models are established by using radial basis function(RBF) neural network based on nearest neighbor clustering algorithm(NNCA) and autoregressive integrated moving average(ARIMA).

By analyzing nearest neighbor clustering algorithm, a new nearest neighbor clustering algorithm is proposed.

The new algorithm brings in the Nearest Neighbor Clustering Algorithm to initialize the number and center of clustering.

The RBF network based on improved nearest neighbor-clustering algorithm is introduced at first.

The nearest neighbor-clustering algorithm has a short training time,less work to calculate and the number of hidden units is not to be determinated in advance in the various RBFNN learning algorithms,the network is optimization after clustering and can be trained on-line,it is an adaptive clustering algorithm for nonlinear real-time system.

Simulation results show that the enhanced nearest neighbor algorithm can achieve better positioning accuracy than the existing nearest neighbor algorithms.

From protein sequences,the encoding method of EBGW(Encoding Based on Grouped Weight)is applied to protein function prediction associated with the nearest neighbor algorithm.

The general situation and basic principle of Radio Frequency Identification(RFID)technology are introduced,according to which the nearest neighbor algorithm for indoor location system LARNDMARC based on RFID is proposed.