@conference {
	title = {Convergence of a Neural Network Classifier},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1990},
	month = {12/1990},
	abstract = {In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm performs stochastic approximation. Convergence of the Voronoi vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also present a modification to the learning algorithm which we argue results in convergence of the LVQ~for a larger set of initial conditions. Finally, we show that LVQ~is a general histogram classifier and that its risk converges to the Bayesian optimal risk as the appropriate parameters go to infinity with the number~of past observations.},
	address = {Honolulu, Hawaii},
	booktitle = {The 29th Conference on Decision and Control (CDC)},
	pages = {pp. 1735-1740}
}
@conference {
	title = {Real-Time Control for a Zero Gravity Robotic End Effector},
	author = {Teolis, Carole A., Baras, John S.},
	year = {1990},
	month = {12/1990},
	abstract = {The ability to easily manipulate objects in a zero gravity environment will play a key role in future space activities. Emphasis will be placed on robotic manipulation. This will serve to increase astronaut safety and utility in addition to several other benefits. It is the aim of this research to develop control laws for the zero gravity robotic end effector designed by engineers at NASA Goddard. A hybrid force/position controller will be used. Sensory data available to the controller are obtained from an array of strain gauges and a linear potentiometer. Applying well known optimal control theoretic principles, the control which minimizes the transition time between positions is obtained. A robust force control scheme is developed which allows the desired holding force to be achieved smoothly without oscillation. In addition, an algorithm is found to determine contact force and contact location.},
	address = {Honolulu, Hawaii},
	booktitle = {The 29th Conference on Decision and Control (CDC)},
	pages = {pp. 3138-3142}
}
@conference {
	title = {Fast Error-Free Algorithms for Polynomial Matrix Computations},
	author = {Baras, John S., MacEnany, David C., Munach, Robert Lawrence.},
	year = {1990},
	month = {12/1990},
	abstract = {Matrices of polynomials over rings and fields provide a unifying framework for many control system design problems. These include dynamic compensator design, infinite dimensional systems, controllers for nonlinear systems, and even controllers for discrete events stems. An important obstacle for utilizing these powerful mathematical tools in practical applications has been the non-availability of accurate and efficient algorithms to carry through the precise error-free computations required by these algebraic methods. In this paper we develop highly efficient, error-free algorithms, for most of the important computations needed in near systems over fields or rings. We show that the structure of the underlying rings and modules is critical in designing such algorithms.},
	address = {Honolulu, Hawaii},
	booktitle = {The 29th Conference on Decision and Control (CDC)},
	pages = {pp. 941-946}
}
@conference {
	title = {Bayesian Sequential Hypothesis Testing},
	author = {Baras, John S., MacEnany, David C.},
	year = {1990},
	month = {08/1990},
	abstract = {We consider the Bayesian sequential detection problem for general observation processes with a continuous time parameter. Primary emphasis is placed on first exit policies and their generic optimality. A new geometric formulation and solution to the existence and uniqueness of the optimal first exit policy will be given as well as an explicit constructive algorithm for its computation. We will apply these resuits to the problem of Bayesian sequential detection for diffusion-type observations although our methods and results generalize to problems of optimal stopping and decision for partially observed Markov chains where the observation processes are of the diffusion or point process type. These results will appear elsewhere.},
	address = {Tallin, USSR},
	booktitle = {The 11th IFAC World Congress},
	pages = {pp. 227-232}
}
@conference {
	title = {Convergence of the Vectors in Kohonen{\textquoteright}s Learning Vector Quantization},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1990},
	month = {07/1990},
	abstract = {Kohonen{\textquoteright}s Learning Vector Quantization is a nonparametric classification scheme which classifies observations by comparing them to k templates called Voronoi vectors. The locations of these vectors are determined from past labeled data through a learning algorithm. When learning is complete, the class of a new observation is the same as the class of the closest Voronoi vector. Hence LVQ is similar to nearest neighbors, except that instead of all of the past observations being searched only the k Voronoi vectors are searched.

In this paper, we show that the LVQ learning algorithm converges to asymptotically stable zeros of an ordinary differential eq{\"u}ation. It is shown that the learning algorithm performs stochastic approximation. Convergence of the vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also~present a modification to the learning algorithm which results in more robust convergence.},
	address = {Paris, France},
	booktitle = {The  1990 International Conference on Neural Networks},
	pages = {pp. 1028-1031}
}
@conference {
	title = {Nonlinear Filtering Asymptotics, Large Deviations and Observers for Nonlinear Systems},
	author = {Baras, John S., James, Matthew R.},
	year = {1990},
	month = {04/1990},
	address = {Santa Barbara, CA},
	booktitle = {NSF-UC-NASA Workshop on Nonlinear Control},
	pages = {pp. 29-30},
	organization = {UCSB}
}
