2,543 research outputs found
Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction
Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization in order to improve its generalization performance. The MCSA begins with an initial population of cuckoo eggs, which represent the translation vectors of the wavelet hidden nodes, and subsequently refines their locations by imitating the breeding mechanism of cuckoos. The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. The feasibility of the proposed method is evaluated by forecasting a benchmark chaotic time series, and its superior prediction accuracy compared with that of conventional WNNs demonstrates its potential benefit
Bat Algorithm: Literature Review and Applications
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.Comment: 10 page
A modified weight optimisation for higher-order neural network in time series prediction
Most of time series signals are difficult to predict as consist of non-linear, high
complexity (noise) and chaotic processes. The challenges in time series prediction
are to provide a technique to better understand a dataset. In line with this, the Cuckoo
Search (CS) learning algorithm, a kind of metaheuristics techniques employs
high-level techniques for exploration and exploitation of the search space in which
its step length is much longer in the long run. Thus, can explicitly being used to
address the possibilities of stochastic trends in time series signals. Since its
discovery, the CS has been used extensively. However, these methods fixed the
parameter values which essential for adjusting the weights. Therefore, a modification
was made by the additional step of information exchange between the top eggs,
which significantly improve the convergence rate. Hence, motivated by the
advantages of those Modified Cuckoo Search (MCS), the improvement of the MCS
called Modified Cuckoo Search-Markov chain Monté Carlo (MCS-MCMC) learning
algorithm is proposed for weight optimisation. As the Markov chain Monté Carlo can
replace the cumbersome in generating the objective functions, it is used to substitute
the Lévy flight found in the MCS’s structure to prove that MCS-MCMC is suitable
for predictive tasks. The performance of MCS-MCMC learning algorithm was
validated with several test functions and compared with those of MCS learning
algorithm. The MCS-MCMC results is further benchmarked with the standard
Multilayer Perceptron, standard Pi-Sigma Neural Network (PSNN), Pi-Sigma Neural
Network-Modified Cuckoo Search, Pi-Sigma Neural Network-Markov chain Monté
Carlo, standard Functional Link Neural Network (FLNN), Functional Link Neural
Network-Modified Cuckoo Search and Functional Link Neural Network-Markov
chain Monté Carlo which emphasis in optimising the accuracy rate. The simulation
results proved that MCS-MCMC outperformed in the form of Accuracy with the
range of 0.003% to 4.421% when incorporated with standard PSNN and FLNN for
three (3) data partitions covering 10 benchmarked time series datasets
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