Regression analysis with fuzzy knowledge representation

ABSTRACT

An algorithm for fuzzy non-linear estimation and an APL implementation of this algorithm are discussed. The algorithm is based on using probability distributions for experimental data. The parameters of the distributions are given as linguistic variables by an expert.

REFERENCES

• 1: Zadeh, L.A. Fuzzy Sets. Information and Control, 1965, v.8, p.338.
• 2: Borisov, A. N., A. V. Alekseev, G. V. Merkuryeva. Fuzzy Information Processing in the Decision- Making Systems. Moscow, "Radio and Connections", 1989.
• 3: Nahmias, S. Fuzzy Variables and Fuzzy Sets. Systems. 1978. Vol. 1, No. 2. pp. 97-110.
• 4: Averkin, A. N., I. Z. Baturshin, A. F. Blishun, V. B. Silov, V. B. Tarasov. Fuzzy Sets in Models of Control and Artificial Intelligence. Moscow, "Science", 1986.
• 5: Efron, B. The Jackknife, the Bootstrap and other Resampling Plans. Philadelphia, Pa.: SIAM, 1982.
• 6: Kozlov, F. A. and Yu. I. Zagorulko Species Solubility in Sodium. Obninsk, IPPE-510 (1974)
• 7: Claar, T.D. Reactor Technology. 1970. Vol. 13, No. 2. p.124.
• 8: Louneva, I. N., V. A. Momzov, S. M. Obraztsov, and A. L. Shimkevich. Solubility Curve Bootstrap Classification. The Third International Seminar on Simulation of Devices and Technologies. Abstracts. Obninsk, 1994

INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: APL

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Statistical computing

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based)

General Terms:
Algorithms, Experimentation, Human Factors, Languages