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We analyze the use of clustering methods for the automatic identification of fuzzy inference models for autoregressive prediction of time series. A methodology that combines fuzzy methods and residual variance estimation techniques is followed. A nonparametric residual variance estimator is used for a priori input and model selection. A simple scheme for(More)
Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept(More)
Substantial ecological changes occurred in the 1970s in the Northern Baltic during a temporary period of low salinity (S). This period was preceded by an episodic increase in the rainfall over the Baltic Sea watershed area. Several climate models, both global and regional, project an increase in the runoff of the Northern latitudes due to proceeding climate(More)
We propose an automatic methodology framework for short-and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems.(More)
We apply fuzzy techniques for system identification and supervised learning in order to develop fuzzy inference based autoregressors for time series prediction. An automatic methodology framework that combines fuzzy techniques and statistical techniques for nonparametric residual variance estimation is proposed. Identification is performed through the learn(More)
This paper proposes an approach to the identification of evolving fuzzy Takagi–Sugeno systems based on the optimally pruned extreme learning machine (OP-ELM) methodology. First, we describe ELM, a simple yet accurate learning algorithm for training single-hidden layer feed-forward artificial neural networks with random hidden neurons. We then describe the(More)
We address, by means of fuzzy linguistic summaries, two related problems: summarizing network flow statistics and making these statistics human-readable. Two complementary summarization methods are developed. First, a fixed set of protoforms of interest is defined, and the ones with a higher truth value are shown to the user as simple on-line summaries.(More)
Between 1990 and 2007, 15 southern white (Ceratotherium simum simum) and black (Diceros bicornis) rhinoceroses on average were killed illegally every year in South Africa. Since 2007 illegal killing of southern white rhinoceros for their horn has escalated to >950 individuals/year in 2013. We conducted an ecological-economic analysis to determine whether a(More)