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It also explains soft computation techniques such as the computational geometric method and least squares approximation that can be effective in monitor design.
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Although these models are compliant with uncertain information management, such as Bayesian, soft computing, and evolutionary computation modeling, the resulting maps are not ratified as "institutional" by the authorities and only can be used as supplemental information that is complementary to the institutional maps.
An affine T-S fuzzy system is derived naturally from linearizing a nonlinear system or from some data-driven identification techniques, for example, cluster-based algorithms or soft-computation learning structures.
However, this method was not as efficient as the new soft computing technique, such as artificial neural networks (ANNs), evolutionary computation (EC), fuzzy logic (FL) and genetic programming, which have been successfully applied in water engineering problems in the last two decades.
Also, Neuro computing and evolutionary computation usually need a lot of computational time, which is the disadvantage of the implementation of soft computing (Dote and Ovaska 2001).
Soft computing principles such as fuzzy logic, evolutionary and nature inspired computations may be applied to analyze dynamically varying data.
Initially, soft computing is composed of three main branches: fuzzy systems [20, 21], evolutionary computation [22, 23], artificial neural computing [24].
In the recent years, the soft computing techniques are increasingly applied in many fields of civil engineering due to their capabilities in computation and knowledge processing.
Soft computing.
Soft-computing approaches have then been successfully applied to solve the SSRP in the past, such as evolutionary computation techniques, variable neighborhood approaches or tabu search algorithms.
This will ensure a minimum number (n3 ≤ m3) of STBC codewords to be considered by the soft demapper for the computation of the APP values of each GF q) symbol, and so will contribute to the reduction of the complexity of soft ML demapping as proposed in Section 4.2, but to the detriment of limiting the maximum channel selectivity that can be achieved within one GF q) symbol.
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