Thesis available on ftp

[Venugopal]

   The following thesis is available on ftp from neuroprose archive:


                   LEARNING IN CONNECTIONIST NETWORKS 
                       USING THE ALOPEX ALGORITHM

		           K. P. Venugopal
		     Florida Atlantic University


        Abstract:

	The ALOPEX algorithm is presented as a `universal' learning
	algorithm for connectionist models. It is shown that the ALOPEX
	procedure can be used efficiently as a supervised learning algorithm
	for such models. The algorithm is demonstrated successfully on a 
	variety of network architectures. Such architectures include multi-
	layered perceptrons, time-delay models, asymmetric fully recurrent
	networks and memory neurons. The learning performance as well as the
	generalization capability of the ALOPEX algorithm, are compared with
	those of the backpropagation procedure, concerning a number of
	benchmark problems, and it is shown that the ALOPEX has specific
	advantages. Results on the MONKS problems are the best reported
	ones so far.

	Two new architectures are proposed for the on-line, direct adaptive
	control of dynamical systems using neural networks. The proposed 
	schemes are shown to provide better response and tracking
        characteristics, than the other existing direct control schemes.
	A velocity reference scheme is introduced to improve the dynamic
	response of on-line learning controllers.

	The proposed learning algorithm and architectures are also studied on
	three practical problems: (i) classification of handwritten digits
	using Fourier descriptors, (ii) recognition of underwater targets
	from sonar returns, conidering temporal dependencies of consecutive
	returns, and (iii) on-line learning control of autonomous underwater
	vehicles, starting from random initial conditions. Detailed studies
	are conducted on the learning control applications. Also, the ability 
	of the neural network controllers to adapt to slow and sudden varying 
	parameter disturbances and measurement noise is studied in detail. 


	---------------------
	Some of the related papers:


	K. P. Venugopal, A. S. Pandya and R. Sudhakar, 'A recurrent neural
	network controller and learning algorithm for the on-line learning
	control of autonomous underwater vehicles', to appear in Neural
	Networks (1994)

	K. P. Venugopal, R. Sudhakar and A. S. Pandya, 'On-line learning
	control of autonomous underwater vehicles using feedforward neural
	networks', IEEE Journal of Oceanic Engineering, vol. 17 (1992)

	K. P. Venugopal, R. Sudhakar and A. S. Pandya, 'An improved scheme
	for the direct adaptive control of dynamical systems using
	backpropagation neural networks' to appear in Circuits, Systems and
	Signal Processing (1994)

	K. P. Venugopal and S. M. Smith, 'Improving the dynamic response of
	neural network controllers using velocity reference feedback'
	IEEE Trans. on Neural Networks, vol. 4, (1993)

	K. P. Unnikrishnan and K. P. Venugopal, 'Alopex: a correlation
	based learning algorithm for feedforward and feedback neural 
	networks' to appear in Neural Computation, vol. 6, (1994)

	A. S. Pandya and K. P. Venugopal, 'A stochastic parallel algorithm
	for learning in neural networks', to appear in IEICE Transactions 
	on Information Processing (1994)

	-----------------------------------------

	The files at archive.cis.ohio-state.edu are

	 venugopal.thesis1.ps.Z
	 venugopal.thesis2.ps.Z
	 venugopal.thesis3.ps.Z
	 venugopal.thesis4.ps.Z
	 venugopal.thesis5.ps.Z
	 venugopal.thesis6.ps.Z
	 venugopal.thesis7.ps.Z

	(total 200 pages)


	to ftp the files:

	unix> ftp archive.cis.ohio-state.edu

	Name (archive.cis.ohio-state.edu:xxxxx): anonymous
	Password: your address

	ftp> cd pub/neuroprose/Thesis
	ftp> binary
	ftp> mget venugopal.thesis*


	uncompress the files after transfering to your machine.
        
	unix> uncompress venugopal*


	-------------------------------------------------

	K. P. Venugopal
	Medical Image Processing Group
	University of Pennsylvania
	423 Blockley Hall
	Philadelphia, PA 19104   	   (venu at pixel.mipg.upenn.edu)