Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

“…Moreover, the applied PSO algorithm uses constant parameters, which requires an extra time-consuming optimization step. Shen et al (2011) introduce a novel hybrid technique which applies an Artificial Fish Swarm algorithm to train Radial Basis Function Neural Networks for modeling the Shanghai Composite Indices. The prediction results are extremely good, but the artificial fish swarm algorithm is not used for the optimization of the RBF network's structure and it requires some parameters to be tuned via a time consuming trial and error approach.…”

“…More specifically our proposed architecture is fully adaptive something that decreases the numbers of parameters that the practitioner needs to experiment while on the other hand it increases the forecasting ability of the network. The proposed methodology is superior in comparison to the application of meta-heuristic methods (PSO, Genetic Algorithms, Swarm Fish Algorithm) that have been already presented in the literature (Nekoukar and Beheshti (2010) and Shen et al (2011)) because it eradicates the risk of getting trapped into local optima and the final solution is assured to be optimal for a subset of the training set.…”

Quantitative Analysis of Dynamic Behaviours of Rural Areas at Provincial Level Using Public Data of Gross Domestic Product

“…The AFSAVP work-flow is given in Figure 2, which is similar to the basic AFSA [23,24]. The AFSAVP includes five steps of operations: (1) behaviour selection; (2) searching behaviour; (3) swarming behaviour; (4) following behaviour and (5) bulletin.…”

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

“…In this context, feed forward neural networks (Wang et al, 2005), parameter estimation in engineering systems (Li et al, 2004), combinatorial optimization problem (Cai, 2010), global optimization (Yang, 2010), Augmented Lagrangian fish swarm based method for global optimization (Rocha et al, 2011), forecasting stock indices using radial basis function neural networks optimized (Shen et al, 2011), and hybridization of the FSA with the Particle Swarm Algorithm to solve engineering systems (Tsai & Lin, 2011).…”