Addenda of Neuro-Fuzzy and Soft Computing

by J.-S. R. Jang, C.-T. Sun, and E. Mizutani


  1. Addendum to Section 5.2, "Basics of Matrix Manipulation and Calculus":
    List of handy formulas for gradients and Jacobian
  2. Addendum to Section 5.2, "Basics of Matrix Manipulation and Calculus":
    Formulas for matrix inversion in block form
  3. Addendum to Section 5.5, "Recursive Least-Squares Estimator":
    Incremental formula for LSE error measures
  4. Addendum to Section 5.5, "Recursive Least-Squares Estimator":
    Recursive LSE in the number of parameters
  5. Addendum to Section 5.8, "LSE for Nonlinear Models":
    List of nonlinear models that can be transformed into linear ones
  6. Addendum to Ch 10.9.2:
    Sec10.9.2.ps (PostScript file)
    This is Eiji Mizutani's paper at WCCI (World Congress on Computational Intelligence) 1998. The paper points out a connection between reinforce learning and recurrent NNs to solve non-Markovian problems. A simple concrete simulation example is described in the paper.
  7. Addendum to "Policy-only Learning" in Chapter 10:
    Sample path-based policy-only learning by actor neural networks
  8. The following two papers are supplementary materials on the trust-region methods on page 139 and Section 6.8:

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