Exploring real domain problems on the second generation neural network
by Amit Gupta; Bipin Kumar Tripathi; Vivek Srivastava
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 26, No. 2, 2023

Abstract: This paper presents a competitive performance of second generation neural network (CVNN) on the two dimensional space over first generation neural network (RVNN) on single dimensional space. The real datasets problems are selected for proposed research work. The second-generation neural network is based on the theory of complex number. Complex numbers are forms of subset of real numbers having magnitude and phase to represent a real valued phenomenon. For the testing and training of real valued problems in complex domain, a mathematical approach Hilbert transformation is used to convert all the real valued data in complex form by sifting the phase by ±90 degree with same amplitude. For learning the network RBP and CBP algorithm is used over proposed benchmark datasets (both real and complex) to train the neural network. A new complex activation function (amplitude and phase type) is utilising by second generation neural network (CVNN). The results show improved efficacy and minimum number of learning cycles for Second generation neural network in complex domain over the first generation neural network in real domain.

Online publication date: Tue, 28-Nov-2023

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