EQUAL INTERVAL RANGE APPROXIMATION AND EXPANDING LEARNING RULE FOR MULTI-LAYER PERCEPTRONS AND APPLICATIONS

In this paper,we propose an equal interval range approximation and expandinglearning rule for multi-layer perceptrons applied in pattern recognitions.Compared with tra-ditional BP algorithm,this learning rule requires the output activations interval between themaximum target output node and other nodes to exceed a given equal interval range for eachtraining input pattern,thus it can train networks faster in much lower calculation cost andmay avoid the occurrences ot reversed target output and overlearning,hence it can improve thenetwork's generalization abilities in pattern recognitions.Through gradually expanding of theinterval range,this learning rule can also enable the network to learn its targets more accuratelyin less additional training iterations.Finally,we apply this algorithm in network training inEEG detection,and the experimental results have shown the above advantages of the proposedalgorithm.

Equal interval range approximation and expanding learning rule for multi-layer perceptrons and applications

In this paper,we propose an equal interval range approximation and expanding learning rule for multi-layer perceptrons applied in pattern recognitions.Compared with tra- ditional BP algorithm,this learning rule requires the output activations interval between the maximum target output node and other nodes to exceed a given equal interval range for each training input pattern,thus it can train networks faster in much lower calculation cost and may avoid the occurrences ot reversed target output and overlearning,hence it can improve the network's generalization abilities in pattern recognitions.Through gradually expanding of the interval range,this learning rule can also enable the network to learn its targets more accurately in less additional training iterations.Finally,we apply this algorithm in network training in EEG detection,and the experimental results have shown the above advantages of the proposed algorithm.