Sigma-Pi Cascade Extended Hybrid Neural Network
Eduardo Masato Iyoda*, Kaoru Hirota* and Fernando J. Von Zuben**
*Department of Computational Intelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8502 Japan
**Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering, State University of Campinas, P.O. Box 6101, Campinas-SP, 13083-970 Brazil
A nonparametric neural architecture called the Sigma-Pi Cascade extended Hybrid Neural Network σπ-(CHNN) is proposed to extend approximation capabilities in neural architectures such as Projection Pursuit Learning (PPL) and Hybrid Neural Networks (HNN). Like PPL and HNN, σπ-CHNN also uses distinct activation functions in its neurons but, unlike these previous neural architectures, it may consider multiplicative operators in its hidden neurons, enabling it to extract higher-order information from given data. σπ-CHNN uses arbitrary connectivity patterns among neurons. An evolutionary learning algorithm combined with a conjugate gradient algorithm is proposed to automatically design the topology and weights of σπ-CHNN. σπ-CHNN performance is evaluated in five benchmark regression problems. Results show that σπ-CHNN provides competitive performance compared to PPL and HNN in most problems, either in computational requirements to implement the proposed neural architecture or in approximation accuracy. In some problems, σπ-CHNN reduces the approximation error on the order of 10-1 compared to PPL and HNN, whereas in other cases it achieves the same approximation error as these neural architectures but uses a smaller number of hidden neurons (usually 1 hidden neuron less than PPL and HNN).
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.