Table 1: Central characteristics of soft computing technologies.

Fuzzy setsArtificial neural networksEvolutionary computing, GAProbabilistic reasoningChaotic computing

Weaknesses(i) Knowledge acquisition
(ii) Learning
Black box interpretability(i) Coding
(ii) Computational
(i) Limitation of the axioms of probability theory
(ii) Lack of complete knowledge
(iii) Computational complexity
(i) Computational complexity
(ii) Chaos identification complexity

Strengths(i) Interpretability
(ii) Transparency
(iii) Plausibility
(iv) Graduality
(v) Modeling
(vi) Reasoning
(vii) Tolerance to imprecision
(i) Learning
(ii) Adaptation
(iii) Fault tolerance
(iv) Curve fitting
(v) Generalization ability
(vi) Approximation ability
(i) Computational efficiency
(ii) Global optimization
(i) Rigorous framework
(ii) Good understanding
(i) Nonlinear dynamics simulation
(ii) Discovering chaos in observed data (with noise)
(iii) Determining the predictability
(iv) Prediction strategies formulation