Artificial Neural Network (ANN) provides the learning ability for solving complex problems in similitude to the human brain. The research gives a detailed descriptive design of a simulated ANN. It expounded the architectural attributes (the network structure) showing the number and topology of the neurons with their interconnectivity and the neuro-dynamic attributes by employing the Levenberg Marquardt back propagation for the training and Gradient descent for the learning (adjusting the individual weight of the connection links). The Simulink in MATLAB was used to simulate the model and implemented with the stock data. The optimum performance for the number of hidden neurons was tested for 1000 epochs and the (4-50-5-1) structure had the least MSE training time of 1.4%. The performance of the model was evaluated using RMSE and it returned an error rate of approximately 0.0004. This shows the capability of the simulated ANN model in prediction.
References
[1]
Huang, Y., Lan, Y., Thomson, S., Fang, A., Hoffmann, W. and Lacey, R. (2010) Development of Soft Computing and Applications in Agricultural and Biological Engineering. Computers and Electronics in Agriculture, 71, 107-127. https://doi.org/10.1016/j.compag.2010.01.001
[2]
Haag, S., Cummings, M. and Dawkins, J. (1998) Management Information System for the Information Age. Mc-Graw-Hill, USA, 526.
[3]
Haykin, S. (1998) Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, Inc., London, USA, 1-41.
[4]
Fausett, L. (1996) Fundamentals of Neural Network: Architectures, Algorithms and Applications, Prentice Hall, Upper Saddle River, New Jersey, 1-14.
[5]
Kehinde, A.J., Adeniyi, A.E., Ogundokun, R.O., Gupta, H. and Misra, S. (2022) Prediction of Students’ Performance with Artificial Neural Network Using Demorgraphic Traits. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illes, Z. and Verma, C., Eds., Recent Innovation in Computing. Lecture Notes in Electrical Engineering, Springer, Singapore, 613-624. https://doi.org/10.1007/978-981-16-8892-8_46
[6]
Lu, C.J., Li, S.H. and Lu, Z.J. (2022) Building Energy Prediction Using Artificial Neural Network: A Literature Survey. Energy and Building, 262, Article 111718. https://doi.org/10.1016/j.enbuild.2021.111718
[7]
Suryani, I. and Buani, D.C.P. (2020) Stock Price Prediction Using Artificial Neural Network Integrated Moving Average. Journal of Physics: Conference Series, 1641, Article 012028. https://doi.org/10.1088/1742-6596/1641/1/012028
[8]
Abhijit, S.P. and Macy, R. (2000) Pattern Recognition with Neural Networks in C . CRC Press, Boca Raton, Florida, 1-43.
[9]
Krose, B. and Van Der Smagt, P. (1996) An Introduction to Neural Networks. 8th Edition, University of Armsterdan, Amsterdam. http://prolland.free.fr/works/ai/docs/neuro-intro.pdf
[10]
Samarasinghe, S. (2006) Neural Networks for Applied Sciences and Engineering from Fundamentals to Complex Pattern Recognition, Auerbach Publications, Taylor and Francis Company, New York. https://doi.org/10.1201/9781420013061
[11]
Kartalopoulous, S.V. (1996) Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications, IEEE Press, Piscataway Township.
[12]
Hill, J.M. (2001) Introduction to Simulink. https://www.slideserve.com/wilona/introduction-to-simulink
[13]
Hagan, M.T., Demth, H.B. and Beale, M. (1996) Neural Network Design. PWS Publishing Company.
[14]
Mathworks (2024) Purelin Linear Transfer Function. https://www.mathworks.com/help/deeplearning/ref/purelin.html
Azhar, K.A. and Kunid, W. (2007) Daily Groundwater Level Fluctuation Forecasting Using Soft Computing Technique. Journal of Nature and Science, 5.
[17]
Rehman, M.Z. and Nawi, N.M. (2011) Improving the Accuracy of Gradient Descent back Propagation Algorithm (GDAM) on Classification Problems. International Journal on New Computer Architectures and Their Applications, 1, 838-847.
[18]
Vernier (2011). What Are Mean Squared Error and Root Mean Squared Error? http://www.vernier.com/til/1014/
[19]
Jadoon, R. (2010) Multiplexer and De-Multiplexer. https://jadoon956.wordpress.com/wp-content/uploads/2010/10/multiplexer-demultiplexer.pdf
