Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Neural networks: applications in industry, business and science
Communications of the ACM
A combined neural network approach for texture classification
Neural Networks
Regression neural network for error correction in foreign exchange forecasting and trading
Computers and Operations Research
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Genetic Feature Selection for Optimal Functional Link Artificial Neural Network in Classification
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Development and performance evaluation of FLANN based model for forecasting of stock markets
Expert Systems with Applications: An International Journal
A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN
Neural Computing and Applications - Special Issue - KES2008
Evolutionarily optimized features in functional link neural network for classification
Expert Systems with Applications: An International Journal
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Expert Systems with Applications: An International Journal
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolution of functional link networks
IEEE Transactions on Evolutionary Computation
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.