The signal noise can be processed via a nonlinear input-output response, causing sufficient signal distortion to transform unimodal signal distributions into bimodal ones [ 2].
Here, we combine three different aspects of multivariate metamodelling: 1) Description of highly nonlinear input-output relationships by regional metamodelling, 2) NPLSR, allowing a retention of a tensor data structure throughout the analysis and 3) Inverse metamodelling in addition to the classical approach, providing more confident conclusions and a more comprehensive model overview.
Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation.
This computation is an example of a hidden layer (or basis function) network performing a nonlinear operation on its inputs, and is made possible by the nonlinear input-output function of the ICcl neurons [50] [52].
A global linearization of nonlinear input-output descriptions by relaxing the appropriate appearances of inputs, outputs and their derivatives is applied.
Type-2 FLCs are also able to realize more complex nonlinear input-output control relationships than a type-1 FLC [ 40, 41] which can be suitable for nonlinear biomedical control processes such as anesthesia regulation.
Efficiency is determined by a ratio of input:output.
Blowfly photoreceptors were used to illustrate information processing by a nonlinear system through input-output analysis.
The effectiveness of the optimal test signal design is demonstrated by the system identification and validation of a nonlinear multiple-input single-output (MISO) automotive engine fuelling model.
In this paper, a stable adaptive fuzzy sliding mode based tracking control is developed for a class of nonlinear MIMO systems that are represented by input output models involving system uncertainties and external disturbances.
SOFC is a nonlinear, multi-input and multi-output system that is hard to model by traditional methodologies.
