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Humans are very capable of solving many scientific and engineering problems, but during the solution process they have a tendency to make mistakes. For example, humans without computer aided tools, would not be able to design VLSI chips larger than 100 transistors. This imperfection of humans make them very unreliable elements in resilient control systems. There is a tendency of replacing humans with computers using artificial intelligence, expert systems, or methods of computational intelligence. The methods of computational intelligence can be most successful but they have to be used with great care. Limitations of fuzzy and neural networks are presented and it is shown how to avoid these limitations so resilient control systems can be developed. It turns out that often popular training algorithms are not capable of tuning neural networks to proper accuracy without losing generalization abilities. As a consequence, such system of computational intelligence may not work properly for cases which were not used in training. The comparison of different neural network architectures follows and also it is shown how to develop and train close to optimal topologies, so resilient control systems can be developed.