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The simple combustion model helps us to understand the combustion phenomenon in many complex engineering applications such as in aircraft and rocket engines.
We report the Axiomatic approach, which has been used to optimize many complex engineering systems, can be applied towards unravelling the complexity associated with a healthcare system and suggesting optimal design solutions.
High dimensional optimization problems play an important role in many complex engineering area.
The development of smart structures and active noise and vibration control technologies promised to revolutionize the design, construction and, most importantly, the performance of many complex engineering.
Now-a-days, Fuzzy Inference System (FIS) is considered as an effective tool for solution of many complex engineering systems when ambiguity and uncertainly is associated with the systems.
Evidence theory has a strong ability to deal with the epistemic uncertainty, based on which the uncertain parameters existing in many complex engineering problems with limited information can be conveniently treated.
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Neural networks have provided an ideal framework for online identification and control of many complex uncertain engineering systems because of their great flexibility in approximating a large class of continuous maps and their adaptability due to their inherently parallel architecture.
However, the method can be used on many others, complex, engineering systems.
The theoretical foundations of ORFE are of central importance in many complex problems in engineering and science.
In practical science and engineering, many complex systems can be described as singularly perturbed systems with separated two-time-scales driven by random perturbations, for example, chemical reaction dynamics [1], electronic circuits [2] and laser systems [3].
However, this approach can be computationally intensive when applied to complex engineering systems having many uncertain parameters.
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