Chapter 25 - MDHS–LPNN: A Hybrid FOREX Predictor Model Using a Legendre Polynomial Neural Network with a Modified Differential Harmony Search Technique

Abstract

This chapter outlines the use of a high order neural network with learning based on a new meta-heuristic optimization algorithm for developing a hybrid FOREX predictor model. The novelty of the work lies in exposing a high order single layer neural network structured using Legendre polynomials for carving an intelligent FOREX predictor model. Further the unknown parameters of the model are estimated using a Modified Differential Harmony Search (MDHS) technique. Modified differential harmony search technique is a new version of original Harmony Search algorithm, in which the current to best mutation strategy is applied in the pitch adjustment operation and instead of using fixed control parameters, they are adapted iteratively according to their previous successful experience. The modified approach leads to an improvement of the convergence speed of the network as well as the predictive ability of the network. Empirically the proposed model is validated by applying it for prediction of currency exchange rates of US Dollar (USD) against four other currencies: Australian Dollar (AUD), British Pound (GBP), Indian Rupee (INR), and Japanese Yen (JPY). From the model verification, it is demonstrated that the proposed network not only provides a higher degree of forecasting accuracy with MDHS learning technique but also performs statistically better than other evaluated learning techniques included in the study.