**Annotated List of Selected Papers on Neural Nets or
Mind**

The annotated list of downloadable papers here is very far from complete. It does not include the first papers I published on many topics.

But they are all papers easily available in electronic form, with some special value added. This is in addition to the other papers posted on my main “What is Mind?” web page.

**Older
Papers Combining Sensitivity Analysis, Backwards Differentiation and Use
in Training Intelligent Neural Network Systems**

P.Werbos,
Applications of advances in
nonlinear sensitivity analysis, in R.Drenick & F. Kozin (eds), *System Modeling and Optimization: Proc. IFIP
Conf. (1981*__)__,Springer 1982; reprinted in P. Werbos, *The Roots of Backpropagatoin*,
Wiley, 1994.

P.Werbos, *Minimum
Cost Differentiation Methods and their Uses in Understanding, Designing and
Optimizing Complex Systems*, an EIA/DOE “Validation Report”,
1982. This was the last version of the longer report cited in the IFIP paper.
These two papers – especially this longer one – were the two papers
discussed with Charles Smith, mentioned in the historical account in Anderson,
J., and E.Rosenfeld, eds, *Talking Nets*,
MIT Press, 1998.

**Papers Connecting Control
Theory and Neural Networks, addressing stability and basic “vector to
vector” designs**:

P.Werbos, Optimization methods for
brain-like intelligent control, *Proc.
IEEE Conf. CDC*, IEEE, 1995

P. Werbos, Neural Networks for Control: Research Opportunities and Recent
Developments, *Proc. IEEE Conf.
CDC*, IEEE, 2002

P.
Werbos, New Designs for Universal
Stability in Classical Adaptive Control and Reinforcement Learning.* Proc. Int’l Joint Conf. Neural
Networks (IJCNN92)*, IEEE, 1999.

This
provides a more straightforward summary and explanation of the results of my
longer 1998 paper.

**Papers Focused On Practical Applications and Methods
of Neurocontrol – Especially Robotics and Saving Damaged Aircraft:**

P. Werbos, Neurocontrollers,
in J.Webster, ed, *Encyclopedia of Electrical and Electronics
Engineering*, Wiley, 1999. Relatively easy to read, but with crucial
techniques, and examples from Hirzinger’s seminal work in robotics, as
well as other areas I have discussed more often.

P.Werbos, Neural networks and flight control: Overview of capabilities and emerging applications, Proc. Of American Control Conference (ACC), 2001.

P. Werbos, Neural Networks for Flight
Control: A Strategic and Scientific Assessment. In C. Jorgensen, ed., Proc. of Neural Nets for Aero Control
Symp., NASA Ames Research Center, Moffett Field, CA, 16 pp., Aug. 1994.

P.Werbos, Elastic fuzzy logic: a better fit to
neurocontrol and true intelligence, *J.
Intelligent & Fuzzy Systems*, Vol. 1, No.4, 1993. This essentially gives
my version of how to get “white box” rules out of complex
neural-net type training systems. Yager and Fukuda applied equivalent (if less
direct) notions of adaptive fuzzy logic in later years. There is also a *unconstrained*
artificial intelligence could, in principle, be as hard to explain as the ideas
in the mind of an Einstein.

For a more general discussion of learning, robotics and the needs of space solar power, see the NSF/NASA year 2000 workshop report and presentations.

**Papers Focused on More Complex (Still
Subsymbolic) Intelligent Designs**

P.Werbos, Supervised
learning: can it escape its local minimum?, *WCNN93 Proceedings*, Erlbaum, 1993. Reprinted in V. Roychowdhury et
al (eds), *Theoretical Advances in Neural
Computation and Learning*, Kluwer, 1994. This paper still addreses
vector-to-vector learning systems, but it discusses important themes like
syncretism and ways to get closer to some of the thoughts of Karl Pribram about
real neurons.

P.Werbos
& X.Z.Pang, Generalized maze navigation: SRN
critics solve what feedforward or Hebbian nets cannot.*Proc. Conf. Systems, Man and Cybernetics (SMC)* (

X.Z.Pang & P.Werbos, Neural network design for J
function approximation in dynamic programming, *Math. Modelling and Scientific Computing* (a Principia Scientia journal),
Vol. 5, NO.2/3, 1996 (physically 1998, work done in 1994). The paper is long,
but provides a relatively straightforward introduction to some key issues in
training recurrent networks.

P. Werbos, Brain-Like Intelligent Control: From Neural Nets to
True Brain-Like Intelligence**. **In *Proc. World Automation* Conference 1998**.**

P.Werbos, Multiple Models for Approximate
Dynamic Programming and True Intelligent Control: Why and How. In K.
Narendra, ed., *Proc. 10th Yale Conf. on
Learning and Adaptive
Systems*.

P.Werbos, A
Brain-Like Design To Learn Optimal Decision Strategies in Complex Environments,
in M.Karny, K.Warwick and V.Kurkova, eds, *Dealing
with Complexity: A Neural Networks Approach*. Springer, *Brain-Like Computing and Intelligent
Information Systems*. Springer,1998. This is a long and very complex paper,
from which I later pulled out the smaller pieces on Object Nets, Brain-Like
Stochastic Search, and so on – pieces which have great potential value in
advancing the ANN field one step ahead, with important new practical
capabilities, even though they don’t go in one step all the way to the
mouse-brain level.

**Papers
Addressing Links to Neuroscience and Consciousness**

P.Werbos, The brain
as a neurocontroller: New hypotheses and experimental possibilities. In
K.Pribram, ed., *Origins: Brain and
Self-Organization*, Erlbaum, 1994, p.680-706

(I regret that I do not have electronic images of the original figures – though in most cases I have newer and better versions inserted.)

P. Werbos and A. Pellionisz, Neurocontrol
and Neurobiology: New Developments and New Connections. In *Proc. Of the
Internatoinal Joint Conference on Neural Networks* (IJCNN92), IEEE, 1992.

P.Werbos, Optimization:
A Foundation for understanding consciousness. In D.Levine & W. Elsberry
(eds) *Optimality in Biological and
Artificial Networks?*, Erlbaum, 1997

Conversations on the path from mouse-level intelligence to the human level, excerpted from the seminar on “The Evolution of Human Intelligence.”

The full seminar report is available from the Foundation for the Future.