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Unlike previous adaptive sampling strategies, we do not interpolate on the potentially very high dimensional space of inputs to the fine-scale simulation.
To obtain local forecasts in the space of inputs, we select a covariance matrix that has a component with the shape of a Gaussian so as to model the interactions between nearby points.
Our problem, then, is to find coding strategies that minimize the noise entropy at fixed p. In the absence of noise, the coding scheme which maps signals into spikes (or their absence) is a boundary in the d dimensional space of inputs.
$$\end{document} Now suppose that once the network has reached equilibrium, a new input pattern is presented to the network from a different point s′, and that this process is iterated multiple times so that the network samples over the space of inputs.
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Perceptual space of interaction task mirroring control space of input device.
The definition of the space of input stimuli is provided in Appendix 2.
Note that only the projection of the DSP (h_{2}) onto the space of input stimuli can be identified.
The required fuzzy space of input variables is partitioned by fuzzy c-means cluster algorithm and the consequent parameters are identified using the method of least squares.
Therefore, F 2 defines, through a nonlinear transformation, how S is included in the original space of input vectors x n.
In order to reduce the number of fuzzy if then rules, the multidimensional space of input variables (instead of the individual input variable) of fuzzy controllers is partitioned into fuzzy sets.
In other words, the learning process can finish any nonlinear mapping from n-dimension space of input to m-dimension space of output through adjusting the thresholds and weights to meet excepted values.
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