Abstract
In this paper, a hybrid learning algorithm for a Multilayer Percept-rons (MLP) Neural Network using Genetic Algorithms (GA) is proposed. This hybrid learning algorithm has two steps: First, all the parameters (weights and biases) of the initial neural network are encoded to form a long chromosome and tuned by the GA. Second, as a result of the GA process, a quasi-Newton method called BFGS method is applied to train the neural network. Simulation studies on function approximation and nonlinear dynamic system identification are presented to illustrate the performance of the proposed learning algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kadirkamanathan, V., Niranjan, M.: A Function Estimation Approach to Sequential Learning with Neural Networks. Neural Computation 5, 954–975 (1993)
Juang, C.F., Chin, C.T.: An On-Line Self-Constructing Neural Fuzzy Inference Network and its Applications. IEEE Trans. Fuzzy Systems 6, 12–32 (1998)
Widrow, B., Rumelhart, D.E., Lehr, M.A.: Neural Networks Applications in Industry, Business and Science. Communication of the ACM 37, 93–105 (1994)
Narendra, K.S., Kannan, P.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. Neural Networks 1, 4–27 (1990)
Vellido, A., Lisboa, P.J.G., Vaughan, J.: Neural Networks in Business: A survey of applications. Expert Systems with Applications 17, 51–70 (1999)
Siddique, M.N.H., Tokhi, M.O.: Training Neural Networks: Backpropagation vs Genetic Algorithms. In: Proceeding of the International Joint Conference on Neural Networks, vol. 4, pp. 2673–2678 (1999)
Tang, K.S., Chan, C.Y., Man, K.F., Kwong, S.: Genetic Structure for NN Topology and Weights Optimization. In: IEEE Conference Publication, vol. 414, pp. 250–255 (1995)
Leng, G., McGinnity, T.M., Prasad, G.: Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms. IEEE Trans. on Fuzzy Systems 14, 755–765 (2006)
Chen, S., Wu, Y., Luk, B.L.: Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks. IEEE Trans. on Neural Networks 10, 1239–1243 (1999)
Maniezzo, V.: Genetic Evolution of Topology and Weight Distribution of Neural Networks. IEEE Trans. on Neural Networks 5, 39–53 (1994)
Billings, S.A., Zheng, G.L.: Radial Basis Function Network Configuration Using Genetic Algorithms. Neural Networks 8, 877–890 (1995)
Frank, H., Leung, F., Lam, H.K., Ling, S.H., Peter, K., Tam, S.: Tuning of the Structure and Parameters f a Neural Network Using an Improved Genetic Algorithm. IEEE Trans. on Neural Networks 14, 79–88 (2003)
Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE ASSP Magazine 1, 4–22 (1987)
Watrous, R.L.: Learning Algorithms for Connectionist Networks: Applied Gradient Meth-ods for Nonlinear Optimization. In: Proceeding IEEE First Int. Conf. Neural Net., vol. 2, pp. 619–627 (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Er, M.J., Liu, F. (2009). Parameter Tuning of MLP Neural Network Using Genetic Algorithms. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-01216-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01215-0
Online ISBN: 978-3-642-01216-7
eBook Packages: EngineeringEngineering (R0)