A Hybrid Intelligent System Integrating the Cascade Forward Neural Network with Elman Neural Network

Abstract

Cascade forward neural network (CFNN) is a well-known static neural network where the signals move in forward direction only. Dynamic neural network such as Elman neural network (ENN) is built in such a way that allows the signals to travel in both directions. Dynamic neural network has been used widely in various applications such as speech recognition and time series and rarely used in static applications because of their poor performance. This paper proposes to hybrid the CFNN with the ENN to take the advantages of both networks with signals travel in both directions. The proposed system is named as HECFNN, and its effectiveness is evaluated using a number of benchmarks. The benchmarks investigated include datasets of Wine, Ionosphere, Iris, Wisconsin breast cancer, glass and Pima Indians diabetes. Firstly, the performances of the hybrid system are compared with those of the CFNN and ENN. The simulations demonstrate that the proposed hybrid network structure can effectively model both linear and nonlinear static systems with high accuracy. The proposed system achieves an improvement in terms of accuracy as compared to the results of the CFNN and ENN. Secondly, the results are compared with different methods reported by Hoang. It is found that the accuracy of the proposed system is as good as, if not better than, other methods. Thirdly, the HECFNN results are also compared with best results reported from different methods in the literature review. It also found that the HECFNN results are better than those methods in general. Based on the results obtained, the proposed HECFNN system demonstrates better performance, thus justifying its potentials as a useful and effective system for prediction and classification.