Published Dec 28, 2002

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Nelson Obregón-Neira, PhD



This work presents a new model to predict complex time series. It ts based on two concepts coming Irotn both Chaos Theory and Artificial Intelligence. Speciiicelly, it uses both the phase space representation of observables and Artificial Neural Network (ANN) for prcdtcting the resulting variables in such space. For the case when a chaotic dynamics behevior is identifled vía nonlinear time series analysis, the problem reduces to train the ANN. Although it is not required to identify such a bebevior in order to apply the model, it is highly suiteble given the obtained results in this work. In this light, it is noted that for an observable that does not come from a dynamical system sbowing low-dimensional chaos, the results suggest a poor efIlciency in the prediction application. In general, the model implies an optimization problem, since in order to achieve an adequate phase space representation it is necessary to es tima te the embedding dimension (mJand the time delay it). Such parameters elongwttb other two related to the topology ofANN forrn a tetradimensional search space which for this case was explored in an exhaustive way.

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How to Cite
Obregón-Neira, N. (2002). Modelo predictivo unidimensional inspirado en teoría del caos y redes neuronales artificiales. Ingenieria Y Universidad, 6(2), 75–92. Retrieved from