FSCL with varying no. of hidden units.

The application of neural networks to the demodulation of spread-spectrum signals in a multiple-access environment is considered. This study is motivated in large part by the fact that, in a multiuser system, the conventional (matched filter) receiversuffers severe performance degradation as the relative powers of the interfering signals become large (the "near-far" problem). Furthermore, the optimum receiver, which alleviates the near-far problem, is too complex to be of practical use. Receivers based on multi-layer perceptrons are considered as a simple and robust alternative to the optimum solution.The optimum receiver is used to benchmark the performance of the neural net receiver; in particular, it is proven to be instrumental in identifying the decision regions of the neural networks. The back-propagation algorithm and a modified version of it are used to train the neural net. An importance sampling technique is introduced to reduce the number of simulations necessary to evaluate the performance of neural nets.

We discuss in this paper architectures for executing probabilistic rule-bases in a parallel manner,using as a theoretical basis recently introduced information-theoretic models. We will begin by describing our (non-neural) learning algorithm and theory of quantitative rule modelling, followed by a discussion on the exact nature of two particular models. Finally we work through an example of our approach, going from database to rules to inference network, and compare the network's performance with the theoretical limits for specific problems.

McGill University Montreal, Canada H3A2A7 PieroCosi Centro di Studio per Ie Ricerche di Fonetica, C.N.R., Via Oberdan,10, 35122 Padova, Italy ABSTRACT Preliminary results on speaker-independant speech recognition are reported. A method that combines expertise on neural networks with expertise on speech recognition is used to build the recognition systems. For transient sounds, eventdriven propertyextractors with variable resolution in the time and frequency domains are used. For sonorant speech, a model of the human auditory system is preferred to FFT as a front-end module. INTRODUCTION Combining a structural or knowledge-based approach for describing speech units with neural networks capable of automatically learning relations between acoustic properties and speech units is the research effort we are attempting.

Parallelizable optimization techniques are applied to the problem of learning in feedforward neural networks. In addition to having superior convergenceproperties, optimization techniques such as the Polak Ribiere method are also significantly more efficient than the Backpropagation algorithm.These results are based on experiments performed on small boolean learning problems and the noisy real-valued learning problem of handwritten character recognition. 1 INTRODUCTION The problem of learning in feedforward neural networks has received a great deal of attention recently because of the ability of these networks to represent seemingly complex mappings in an efficient parallel architecture. This learning problem can be characterized as an optimization problem, but it is unique in several respects. Function evaluation is very expensive. However, because the underlying network is parallel in nature, this evaluation is easily parallelizable. In this paper, we describe the network learning problem in a numerical framework and investigate parallel algorithms for its solution. Specifically, we compare the performance of several parallelizable optimization techniques to the standard Back-propagation algorithm. Experimental results show the clear superiority of the numerical techniques. 2 NEURAL NETWORKS A neural network is characterized by its architecture, its node functions, and its interconnection weights. In a learning problem, the first two of these are fixed, so that the weight values are the only free parameters in the system.

We propose an optimality principle for training an unsupervised feedforwardneural network based upon maximal ability to reconstruct the input data from the network outputs. Wedescribe an algorithm which can be used to train either linear or nonlinear networks with certain types of nonlinearity. Examples of applications to the problems of image coding, feature detection, and analysis of randomdot stereogramsare presented.

The two-volume set entitled "Knowledge-Based Systems (Volume 1, Knowledge Acquisition for Knowledge-Based Systems, 355 pp., and Volume 2, "Knowledge Acquisition Tools for Expert Systems, 343 pp., Academic Press, San Diego, California, 1988), edited by B. R. Gaines and J. H. Boose, is an excellent collection of papers useful to both commercial practitioners of knowledge-based-systems development and research-oriented scientists at specialized centers or academic institutions.

The vendors Based Systems, 355 pp., and Volume 2, techniques. They are interesting of knowledge-based-systems development Knowledge Acquisition Tools for Expert and informative, particularly tools, for example, Inference, Systems, 343 pp., Academic Press, San "Generalization and Noise" by Y. IntelliCorp, Aion, AI Corp., and IBM, Diego, California, 1988), edited by B. Kodratoff and M. Manango, which would do well to pay heed to these R. Gaines and J. H. Boose, is an excellent discusses symbolic and numeric rule books because they point the way to collection of papers useful to both induction.

Alternative suggestions The call for papers for AAAI-90 requires 68 Willow Road, Menlo Park, CA were discussed. Policy changes each author to specify his or her topic 94025.

The designer's input can be manually added to Design Automation: Automated Full-which itself is awkward) in the The author is criticizing the capability Custom VLSI Layout Using the Ulysses script environment, which considerably of the Weaver system (a knowledge-based Design Environment (Academic Press, reduces the power and authority circuit interconnections Boston, Massachusetts, 1988, 463 of the demonstration. This disappointing router) to restart, continue (that is, to pages) by Michael L. Bushnell deals demonstration might be the be interrupted), or accept that a user with an interesting and challenging result of the project's ambitious nature might specify some routing channels. A The book is misleading in its treatment achieve. The problem here is not the system called Ulysses that implements of some key points. Any routing expert blackboard architecture is described.