A neural network solution is proposed for solving path planning problems The proposed network is a two-dimensional sheetfaced by mobile robots. of neurons forming a distributed representation of the robot's workspace. Lateral interconnections between neurons are "cooperative", so that the network exhibits oscillatory behaviour. These oscillations are used to generate solutions of Bellman's dynamic programming equation in the context of path planning. Simulation experiments imply that these networks locate paths even in the presence of substantial levels of circuitglobal optimal nOlse. 1 Dynamic Programming and Path Planning Consider a 2-DOF robot moving about in a 2-dimensional world. A robot's location is denoted by the real vector, p.

Existing metrics for the learning performance of feed-forward neural networks do not provide a satisfactory basis for comparison because the choice of the training epoch limit can determine the results of the comparison. I propose new metrics which have the desirable property of being independent of the training epoch limit. The efficiency measures the yield of correct networks in proportion to the training effort expended. The optimal epoch limit provides the greatest efficiency. The learning performance is modelled statistically, and asymptotic performance is estimated. Implementation details may be found in (Harney, 1992). 1 Introduction The empirical comparison of neural network training algorithms is of great value in the development of improved techniques and in algorithm selection for problem solving. In view of the great sensitivity of learning times to the random starting weights (Kolen and Pollack, 1990), individual trial times such as reported in (Rumelhart, et al., 1986) are almost useless as measures of learning performance.

Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object theybelong. Current computational systems that perform this operation arebased on predefined grouping heuristics.

I present a modular network architecture and a learning algorithm based on incremental dynamic programming that allows a single learning agent to learn to solve multiple Markovian decision tasks (MDTs) with significant transferof learning across the tasks. I consider a class of MDTs, called composite tasks, formed by temporally concatenating a number of simpler, elemental MDTs. The architecture is trained on a set of composite andelemental MDTs. The temporal structure of a composite task is assumed to be unknown and the architecture learns to produce a temporal decomposition.It is shown that under certain conditions the solution of a composite MDT can be constructed by computationally inexpensive modifications of the solutions of its constituent elemental MDTs. 1 INTRODUCTION Most applications of domain independent learning algorithms have focussed on learning single tasks. Building more sophisticated learning agents that operate in complex environments will require handling multiple tasks/goals (Singh, 1992). Research efforton the scaling problem has concentrated on discovering faster learning algorithms, and while that will certainly help, techniques that allow transfer of learning across tasks will be indispensable for building autonomous learning agents that have to learn to solve multiple tasks. In this paper I consider a learning agent that interacts with an external, finite-state, discrete-time, stochastic dynamical environment andfaces multiple sequences of Markovian decision tasks (MDTs).

Here during the production event a very low infonnational input ("Madonna," and "Matisse") is used

Unfortunately, convergence of normal WTA networks is extremely sensitive to the magnitudes of their weights, which must be hand-tuned and which generally onlyprovide the right amount of inhibition across a relatively small range of initial conditions. This paper presents Dynamjcally Adaptive Winner-Telke-All (DA WTA) netw rls, which use a regulatory unit to provide the competitive inhibition to the units in the network. The DAWTA regulatory unit dynamically adjusts its level of activation during competition to provide the right amount of inhibition to differentiate betweencompetitors and drive a single winner. This dynamic adaptation allows DAWTA networks to perform the winner-lake-all function for nearly any network size or initial condition.

Model-map generated by the SOFM-algorithm.

PA 15261 Abstract Five experiments were performed using several neural network architectures to identify the location of a wave in the time ordered graphical results from a medical test. Baseline results from the first experiment found correct identification of the target wave in 85% of cases (n 20). Other experiments investigated the effect of different architectures and preprocessing the raw data on the results. The methods used seem most appropriate for time oriented graphical data which has a clear starting point such as electrophoresis Or spectrometry rather than continuous teSts such as ECGs and EEGs. I INTRODUCTION Complex wave form recognition is generally considered to be a difficult task for machines. Analytical approaches to this problem have been described and they work with reasonable accuracy (Gabriel et al. 1980.

We present experimental data from an analog CMOS LSI chip that implements the Herault-Jutten adaptive neural network.

We apply this method to the task of controlling the outflow and temperature of a water tank, producing statistically-significant gains in accuracy over both a standard neural network approach and a non-learning PID controller. Furthermore,using the PID knowledge to initialize the weights of the network produces statistically less variation in testset accuracy when compared to networks initialized with small random numbers.