Systems analysis {systems analysis} {system analysis} {system design} tracks inputs, processing, and outputs.
theories
Artificial intelligence, artificial life, catastrophe theory, chaos theory, complexity theory, computational complexity theory, cybernetics, dynamical systems theory, evolution theories, experimental mathematics, fractal geometry, general systems theory, nanotechnology, scientific computing, self-organization, and statistical mechanics are theories about systems and can be analysis tools.
changing
To change system, change weakest part first. Change only one step or thing, then test system behavior [Kampis, 1991].
matrix
Matrices indicate part-pair relation and interaction strength. Hypermatrices can show secondary interactions and synergisms.
iterations
Systems can repeat processes, to converge on solutions. Fast systems, algorithms, and processes are unlikely to use loops and iterations.
goal-driven system
Goals are ideal states to approach or bad states to avoid. Programs measure progress toward goal after each step. Systems {goal-driven system} can try to minimize difference between current situation {origin, state} and desired situation {destination, state}. Systems can try to maximize difference between current situation and undesired situation. Goal-driven systems typically have subgoals and subsystems that lead to main goal.
To reach goal, systems usually use association and recall, rather than logic or statistics.
interactive system
Systems {interactive system} can be complex, chaotic, and unpredictable. They can self-organize, because interactions assume stable patterns. Other parts can assume damaged-part or overloaded-part functions.
Mathematical Sciences>Computer Science>System Analysis
3-Computer Science-System Analysis
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Date Modified: 2022.0224