Teaching–Learning Optimization Based Cascaded Low-Complexity Neural Network Model for Exchange Rates Forecasting

An efficient hybrid forecasting model based on teaching–learning-based optimization cascaded with functional link artificial neural network (CFLANN-TLBO) is proposed in this paper. This hybrid method is mainly used for the prediction of the exchange of currency rates between one US Dollar (USD) to Indian Rupees (INR) and Canadian Dollar (CAD). In cascading FLANN model, computational complexity has reduced as well as the weights of the model optimized by TLBO algorithm to converge faster. The model’s performance is measured by determining the mean absolute percentage error (MAPE). The performance of the proposed model is also compared with other optimization techniques like cat swarm optimization (CSO), particle swarm optimization (PSO), and differential evolution (DE)-based cascaded FLANN. The proposed model performs better in comparison to cat swarm optimization (CSO), particle swarm optimization (PSO), and differential evolution (DE) with a higher accuracy.

[1]  Vivek Patel,et al.  An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems , 2012 .

[2]  Ganapati Panda,et al.  Non-linear dynamic system identification using Cascaded Functional Link Artificial Neural Network , 2009, Int. J. Artif. Intell. Soft Comput..

[3]  Cheung-Wen Chang,et al.  Forecasting Exchange Rates Using Integration of Particle Swarm Optimization and Neural Networks , 2009, 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC).

[4]  Sreejit Chakravarty,et al.  A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices , 2012, Appl. Soft Comput..

[5]  S. Mishra,et al.  Chebyshev Functional Link Artificial Neural Networks for Denoising of Image Corrupted by Salt and Pepper Noise , 2009 .

[6]  Himansu Das,et al.  Classification of Intrusion Detection Using Data Mining Techniques , 2018 .

[7]  Mohammad Teshnehlab,et al.  A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems , 2013 .

[8]  B. B. Misra,et al.  Functional Link Artificial Neural Network for Classification Task in Data Mining , 2007 .

[9]  Sanjeev Kulkarni,et al.  Privacy and Security Issues In Mobile Social Networking and in Modern Shopping Experience , 2013, BIOINFORMATICS 2013.

[10]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[11]  Babita Majhi,et al.  Development and performance evaluation of DE based time series prediction model , 2011, 2011 International Conference on Energy, Automation and Signal.

[12]  Himansu Das,et al.  Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach , 2018 .

[13]  Faissal Mili,et al.  A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification , 2012 .

[14]  John Paul T. Yusiong,et al.  Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm , 2012 .

[15]  Himansu Das,et al.  A Novel PSO Based Back Propagation Learning-MLP (PSO-BP-MLP) for Classification , 2015 .

[16]  Sung-Bae Cho,et al.  An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification , 2012, J. Syst. Softw..