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Nerds and Geeks were scattered across mainland Europe, where they were ruthlessly put to work, inventing things and working out complicated problems for the various governments and ruling parties.
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Analytic Hierarchy Process (AHP) which is a multi- criteria decision making (MCDM) method for addressing complicated problems reduces the waste of time and improves the accuracy of decision making.
This system is helpful to solve complicated problems in designing numerical controls for ATL such as ensuring high requirements for precise machine control and synchronization of motion control and on-off control.
For complicated problems, many sweeps are needed for convergence and the methods become slow [32, 33].
The chapter discusses the fundamental steps in the system approach for solving complicated problems and thus developing corresponding systems.
For complicated problems, artificial neural network ensemble classifier, instead of a single ANN classifier, is considered.
For complicated problems, some of them may need to be applied more than once.
Particle Swarm Optimization (PSO) is an optimization algorithm considered to be highly efficient for the solution of complicated problems.
The P V curve of photovoltaic system exhibits multiple peaks under various conditions of functioning and changes in meteorological conditions which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods and the Particle swarm optimization (PSO) algorithm is considered to be highly efficient for the solution of complicated problems.
The grey model is particularly useful for solving complicated problems that are lack of sufficient information, or need to process uncertain information and to reduce random effects of acquired data.
The AHP provides a flexible, low cost, and easily understood way for analysis complicated problems (Satty 1980).
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