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In this paper introduces the optimization of ensemble neural networks with fuzzy integration type-1 and type-2 for application of the prediction of complex time series, the methods used for optimization are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for optimization of ensemble neural networks for integration of network response is made with typo-1 and type-2 fuzzy systems. The...
This paper describes the optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction of the Dow Jones time series. The Dow Jones time series is used to the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble...
This paper describes the optimization of an ensemble neural network with fuzzy integration of responses based on type-1 and type-2 fuzzy logic. Genetic algorithms are used as method of optimization in this case. The time series that is being considered for the ensemble is the US Dollar/MX Peso exchange rate. Simulation results show that the ensemble approach produces good prediction of the exchange...
One of the most important goals of time series analysis is prediction basing on the analyzed information. But it is not easy to analyze the patterns, regularities and trends of non-stationary and/or chaos time series because their major characteristics are non-linear and vague. In this paper, we propose primary and secondary tuning procedures that can enhance the accuracy for designing fuzzy prediction...
Using time-series data, and testing the value of incorporating genetic algorithm, momentum technique, event-knowledge, selective presentation learning (under the SEL algorithm) and new training criteria (for financial time series), it is demonstrated that significant improvement in predictability accrues in deployment of the Standard Additive Model (SAM) during application of the fuzzy set approach...
This paper presents the adaptation of an evolutionary cooperative competitive RBFN learning algorithm, CO2RBFN, for short-term forecasting of extra virgin olive oil price. The olive oil time series has been analyzed with a new evolutionary proposal for the design of RBFNs, CO2RBFN. Results obtained has been compared with ARIMA models and other data mining methods such as a fuzzy system developed with...
In this paper, we introduce a novel approach to time-series prediction realized both at the linguistic and numerical level. It exploits fuzzy cognitive maps (FCMs) along with a recently proposed learning method that takes advantage of real-coded genetic algorithms. FCMs are used for modeling and qualitative analysis of dynamic systems. Within the framework of FCMs, the systems are described by means...
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