Soft Computing as a Tool, Six Years Later

  • Piero P. Bonissone
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 271)


After a brief expression of my personal gratitude for the inspiring mentorship received from Professors Lotfi Zadeh and Abe Mamdani, I review some of the 70’s seminal papers that lead to Prof Mamdani’s innovation in fuzzy control (FC). Then, I discuss the concurrent development of FC and expert system applications, which took place in the 80’s, noting the similarity stemming from their common use of knowledge bases (KB) developed via rapid prototyping. FC and expert systems also shared a common difficulty: avoiding KB obsolescence over time which was caused by the dynamic environment in which they were deployed. For FC, the lack of automation in their design and maintenance process changed in the 90’s, when Soft Computing (SC) offered a broader computational paradigm for developing intelligent systems, by adding a search and learning component to the fuzzy logic reasoning component. These SC components allowed researchers to automate the fine-tuning of fuzzy systems. In a 2004 position paper, Prof. Mamdani and I made some initial remarks regarding the use and misuse of SC as a tool. In the last six years we have seen an evolution of SC, with a clearer role for their use in capturing knowledge to embed in object-level models, and meta-knowledge to guide the design and upkeep of these models. I illustrate this concept with three real-world examples in insurance risk management, fleet asset selection, and power plant management.


Fuzzy Logic Fuzzy System Fuzzy Control Fuzzy Controller Soft Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.General Electric Global ResearchSchenectadyUSA

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