Neural computing: theory and practice
Neural computing: theory and practice
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Fuzzy Logic and Soft Computing
Fuzzy Logic and Soft Computing
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Handbook of Neural Computation
Handbook of Neural Computation
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Monitoring Complex Systems with Causal Networks
IEEE Computational Science & Engineering
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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We present methods and tools from the Soft Computing (SC) domain, which is used within the diagnostics and prognostics framework to accommodate imprecision of real systems. SC is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We outline the advantages and disadvantages of these methodologies and show how they can be combined to create synergistic hybrid SC systems. We conclude the paper with a description of successful SC case study applications to equipment diagnostics.