Artificial Multi-Polynomial Higher Order Neural Network Models-Reference-Cited by-同舟云学术

Artificial Multi-Polynomial Higher Order Neural Network Models

Published:2013 Issue: Volume: Page:1-29
ISSN:
Container-title:Artificial Higher Order Neural Networks for Modeling and Simulation
language:
Short-container-title:

Author:

Zhang Ming¹

Affiliation:

1. Christopher Newport University, USA

Abstract

This chapter introduces Multi-Polynomial Higher Order Neural Network (MPHONN) models with higher accuracy. Using Sun workstation, C++, and Motif, a MPHONN Simulator has been built. Real world data cannot always be modeled simply and simulated with high accuracy by a single polynomial function. Thus, ordinary higher order neural networks could fail to simulate complicated real world data. However, the MPHONN model can simulate multi-polynomial functions, and can produce results with improved accuracy through experiments. By using MPHONN for financial modeling and simulation, experimental results show that MPHONN can always have 0.5051% to 0.8661% more accuracy than ordinary higher order neural network models.

Publisher

IGI Global

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