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Unlike traditional computing approaches, the parallel computation models take into account both the architecture of the search algorithm and the computation resources together.
Most of these works require formulation of mathematical models which extensively use artificial intelligence techniques, intelligent methods and agent-based computing approaches to solve the computational problems.
To deal with this, parallel computing approaches have been developed to alleviate the computational burden associated with large dataset [18].
Data-intensive computing refers to a type of computation, incorporating systems and applications, which uses parallel and distributed computing approaches to process large volumes of data, currently in the order of terabytes or petabytes.
These processor architectures can in principle be used additively to meet the ever increasing computational demands of MCPSC by complementing already in use distributed computing approaches [ 25].
For one thing, most of the quantum computing approaches only worked when temperatures were cooled to near absolute zero.
However, current redundant computing approaches do not repair failed or corrupted replicas.
Extensive comparison is made using the soft computing approaches for noise cancellation based on associative memories.
Improved fuzzy classifier functions are approximated using statistical and soft computing approaches.
Soft computing approaches using training data are good approximations to model the different systems.
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This paper investigates the identification of nonlinear systems by utilizing soft-computing approaches.
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