Functional Link Neural Network with Modified Cuckoo Search Training Algorithm for Physical Time Series Forecasting

Abstract

Functional link neural network (FLNN) naturally extends the family of theoretical feedforward network structure by introducing nonlinearities in inputs patterns enhancements. It has emerged as an important tool used for function approximation, pattern recognition and time series prediction. The standard learning algorithm used for the training of FLNN is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP algorithm is it tends to easily get trapped in local minima, resulting the degrading performance of FLNN. To overcome this problem, this work proposed an alternative learning scheme for FLNN by using a Modified Cuckoo Search algorithm (MCS), and the model is called FLNN-MCS. The performance of FLNN-MCS is evaluated based on the prediction error, testing on two physical time series data; relative humidity and temperature. Simulation results have shown that the prediction performed by FLNN-MCS is much superior compared to Multilayer Perceptron and FLNN trained with BP, and FLNN trained with Artificial Bee Colony algorithm. The significant performance has proven that FLNN-MCS is capable in mapping the input-output function for the next-day ahead forecasting.