An ]Efficient Multilayer Quadratic Perceptron for Pattern Classification and Function Approximation
نویسندگان
چکیده
Abs t rac t : W e propose an architecture of a multilayer quadratic perceptron (MLQP) that combines advantages of multilayer perceptrons(MLPs) and higher-order feedforward neural networks. The features of MLQP are in its simple structure, practical number of adjustable connection weights and powerful learning ability. I n this paper, the architecture of MLQP is described, a backpropagation learning algorithm for MLQP is derived, and the learning speed of MLQP is compared expen'mentally with M L P and other two kinds of the second-order feedforward neural networks on pattern classification and function approxamation problems.
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