An improved third term backpropagation algorithm – inertia expanded chebyshev orthogonal polynomial

• Published in: Journal of intelligent & fuzzy systems Vol. 37; no. 3; pp. 3795 - 3804
• Main Authors: Sornam, Madasamy, Vanitha, Venkateswaran
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2019; Sage Publications Ltd
• Subjects: Algorithms; Back propagation; Chebyshev approximation; Classification; Convergence; Entrapment; Inertia; Machine learning; Polynomials; Wheat; Back propagation; feed forward neural network; inertia; chebyshev polynomial; momentum
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-190063

The standard backpropagation algorithm has already proven its effectiveness in most of the potential problems, but the major limitation is entrapment of local minima and slow convergence rate. To address these issues, a modified backpropagation algorithm has been proposed by adding a third term called inertia, the physical component used to accelerate the network towards the convergence without getting stuck into local minima. The Chebyshev polynomial form is a convenient method for expanding a function in a linear independent term. Inertia has been expanded using Chebyshev polynomial which is used as a third term in weight updation. The performance of the proposed algorithm outperforms the standard backpropagation algorithm (SBP) and the backpropagation algorithm with momentum (SBPM). The proposed algorithm was tested with the standard benchmark problems such as XOR problem, parity checking problem and dataset from UCI machine learning repository such as iris flower classification, wheat classification, breast cancer detection and wine classification. Experimental results show that the addition of the third parameter called inertia in the backpropagation algorithm gave better performance and faster convergence rate compared to the SBP and SBPM.