In general, control objectives such as the positioning and orienting of the endeffector arespecified with respect to task space coordinates; however, the manipulator is typica1ly controlled only in the configuration space.

In this paper, a tree based neural network viz. MARS (Friedman, 1991) for the modelling of the yield strength of a steel rolling plate mill is described. The inputs to the time series model are temperature, strain, strain rate, and interpass time and the output is the corresponding yield stress. It is found that the MARSbased model reveals which variable's functional dependence is nonlinear, and significant. The results are compared with those obta.ined

Using the double-step target displacement paradigm the mechanisms underlying armtrajectory modification were investigated. Using short (10-110 msec) inter-stimulus intervals the resulting hand motions were initially directed in between the first and second target locations. The kinematic features of the modified motions were accounted for by the superposition scheme, which involves the vectorial addition of two independent point-topoint motionunits: one for moving the hand toward an internally specified location and a second one for moving between that location and the final target location. The similarity between the inferred internally specified locations andpreviously reported measured endpoints of the first saccades in double-step eye-movement studies may suggest similarities between perceived targetlocations in eye and hand motor control.

We develop a model-independent method for characterizing the reliability of neural responses to brief stimuli. This approach allows us to measure the discriminability of similar stimuli, based on the real-time response of a single neuron. Neurophysiological data were obtained from a movementsensitive neuron(HI) in the visual system of the blowfly Calliphom erythrocephala. Furthermore,recordings were made from blowfly photoreceptor cells to quantify the signal to noise ratios in the peripheral visual system. As photoreceptors form the input to the visual system, the reliability oftheir signals ultimately determines the reliability of any visual discrimination task. For the case of movement detection, this limit can be computed, and compared to the HI neuron's reliability. Under favorable conditions,the performance of the HI neuron closely approaches the theoretical limit, which means that under these conditions the nervous system adds little noise in the process of computing movement from the correlations of signals in the photoreceptor array.

We have previously described an unsupervised learning procedure that discovers spatially coherent propertit _; of the world by maximizing the information thatparameters extracted from different parts of the sensory input convey about some common underlying cause. When given random dot stereograms of curved surfaces, this procedure learns to extract surface depthbecause that is the property that is coherent across space. It also learns how to interpolate the depth at one location from the depths at nearby locations (Becker and Hint.oll.

Visual attention is the ability to dynamically restrict processing to a subset of the visual field. Researchers have long argued that such a mechanism is necessary to efficiently perform many intermediate level visual tasks. This paper describes VISIT, a novel neural network model of visual attention.

Two projection based feedforward network learning methods for modelfree regressionproblems are studied and compared in this paper: one is the popular back-propagation learning (BPL); the other is the projection pursuit learning (PPL).

Padhraic Smyth, J eft" Mellstrom Jet Propulsion Laboratory 238-420 California Institute of Technology Pasadena, CA 91109 Abstract We describe in this paper a novel application of neural networks to system health monitoring of a large antenna for deep space communications. The paper outlines our approach to building a monitoring system using hybrid signal processing and neural network techniques, including autoregressive modelling, pattern recognition, and Hidden Markov models. We discuss several problems which are somewhat generic in applications of this kind - in particular we address the problem of detecting classes which were not present in the training data. Experimental results indicate that the proposed system is sufficiently reliable for practical implementation. 1 Background: The Deep Space Network The Deep Space Network (DSN) (designed and operated by the Jet Propulsion Laboratory (JPL)for the National Aeronautics and Space Administration (NASA)) is unique in terms of ...

We present two neural network controller learning schemes based on feedbackerror-learning and modular architecture for recognition and control of multiple manipulated objects. In the first scheme, a Gating Network is trained to acquire of a number of objects (or sets ofobject-specific representations for recognition objects). In the second scheme, an Estimation Network is trained to acquire function-specific, rather than object-specific, representations which directly estimate to identify manipulatedphysical parameters. Both recognition networks are trained objects using somatic and/or visual information. After learning, appropriate motor commands for manipulation of each object are issued by the control networks.

Feedforward networks composed of units which compute a sigmoidal function ofa weighted sum of their inputs have been much investigated. We tested the approximation and estimation capabilities of networks using functions more complex than sigmoids. Three classes of functions were tested: polynomials, rational functions, and flexible Fourier series. Unlike sigmoids,these classes can fit nonmonotonic functions. They were compared on three problems: prediction of Boston housing prices, the sunspot count, and robot arm inverse dynamics. The complex units attained clearlysuperior performance on the robot arm problem, which is a highly nonmonotonic, pure approximation problem. On the noisy and only mildly nonlinear Boston housing and sunspot problems, differences among the complex units were revealed; polynomials did poorly, whereas rationals and flexible Fourier series were comparable to sigmoids. 1 Introduction