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Articles 1 - 9 of 9
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Integrated Neural Network And Machine Vision Approach For Intelligent State Identification, Cihan H. Dagli, Timothy Andrew Bauer
Integrated Neural Network And Machine Vision Approach For Intelligent State Identification, Cihan H. Dagli, Timothy Andrew Bauer
Engineering Management and Systems Engineering Faculty Research & Creative Works
An interfacing of neural networks (NNs) and machine vision to provide the next state of a system given an image of the present state of the system is presented. This interfacing is applied to a loading operation. First, a NN is trained for part recognition under conditions of rotation, location, object distortion, and background noise given an image of the part. Then, a second NN, given the output of the first NN and an image of a pallet being loaded, is trained for optimal part loading onto the pallet under conditions of noise in the image. The paradigm used is …
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
Electrical and Computer Engineering Faculty Research & Creative Works
Summary form only given, as follows. The feasibility of training an adaptive resonance theory (ART-1) network to first cluster aircraft parts into families, and then to recall the most similar family when presented a new part has been demonstrated, ART-1 networks were used to adaptively group similar input vectors. The inputs to the network were generated directly from computer-aided designs of the parts and consist of binary vectors which represent bit maps of the features of the parts. This application, referred to as group technology, is of large practical value to industry, making it possible to avoid duplication of design …
An Empirical Analysis Of Backpropagation Error Surface Initiation For Injection Molding Process Control, Alice E. Smith, Elaine R. Raterman, Cihan H. Dagli
An Empirical Analysis Of Backpropagation Error Surface Initiation For Injection Molding Process Control, Alice E. Smith, Elaine R. Raterman, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Backpropagation neural networks are trained by adjusting initially random interconnecting weights according to the steepest local error surface gradient. The authors examine the practical implications of the arbitrary starting point on the error landscape of the ensuing trained network. The effects on network convergence and performance are tested empirically, varying parameters such as network size, training rate, transfer function and data representation. The data used are live process control data from an injection molding plant
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
Two artificial neural network systems are considered in a hierarchical fashion to plan the trajectory and control of a robotic arm. At the higher level of the hierarchy the neural system consists of four networks: a restricted Coulomb energy network to delineate the robot arm workspace; two standard backpropagation (BP) networks for coordinates transformation; and a fourth network which also uses BP and participates in the trajectory planning by cooperating with other knowledge sources. The control emulation process which is developed using a second neural system at a lower hierarchical level provides the correct sequence of control actions. An example …
Hierarchical Neurocontroller Architecture For Intelligent Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Hierarchical Neurocontroller Architecture For Intelligent Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level neural system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower one provides the correct sequence of control actions. The capabilities, including speed, adaptability, and computational efficiency, of the developed architecture are illustrated by an example.
A Hybrid Optoelectronic Art-1 Neural Processor, Donald C. Wunsch, T. P. Caudell
A Hybrid Optoelectronic Art-1 Neural Processor, Donald C. Wunsch, T. P. Caudell
Electrical and Computer Engineering Faculty Research & Creative Works
Summary form only given. A new implementation of ART-1 (adaptive resonance theory) has been proposed that efficiently combines optical and electronic devices. All parallel computations are performed by the optics, while serial operations are performed in electronics.
A Neural Architecture For Unsupervised Learning With Shift, Scale And Rotation Invariance, Efficient Software Simulation Heuristics, And Optoelectronic Implementation, Donald C. Wunsch, D. S. Newman, T. P. Caudell, R. A. Falk, C. David Capps
A Neural Architecture For Unsupervised Learning With Shift, Scale And Rotation Invariance, Efficient Software Simulation Heuristics, And Optoelectronic Implementation, Donald C. Wunsch, D. S. Newman, T. P. Caudell, R. A. Falk, C. David Capps
Electrical and Computer Engineering Faculty Research & Creative Works
A simple modification of the adaptive resonance theory (ART) neural network allows shift, scale and rotation invariant learning. The authors point out that this can be accomplished as a neural architecture by modifying the standard ART with hardwired interconnects that perform a Fourier-Mellin transform, and show how to modify the heuristics for efficient simulation of ART architectures to accomplish the additional innovation. Finally, they discuss the implementation of this in optoelectronic hardware, using a modification of the Van der Lugt optical correlator
A Commodity Trading Model Based On A Neural Network-Expert System Hybrid, K. Bergerson, Donald C. Wunsch
A Commodity Trading Model Based On A Neural Network-Expert System Hybrid, K. Bergerson, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Demonstrates a system that combines a neural network approach with an expert system to provide superior performance compared to either approach alone. Learning capability is provided in a software-based approach to commodity trading systems. The authors used the backpropagation network with some parameters selected experimentally. They used a human expert to implicitly define patterns, using hindsight, that an intelligent system might have been able to use for an accurate prediction. Desired outputs were found by a combination of observing the behavior of technical indices that normally precede a certain kind of market behavior, and by observing the actual market behavior …
An Optoelectronic Adaptive Resonance Unit, Donald C. Wunsch, T. P. Caudell, R. A. Falk, C. David Capps
An Optoelectronic Adaptive Resonance Unit, Donald C. Wunsch, T. P. Caudell, R. A. Falk, C. David Capps
Electrical and Computer Engineering Faculty Research & Creative Works
The authors demonstrate a hardware implementation of the adaptive resonance theory ART 1 neural network architecture. The optoelectronic ART1 unit, is a novel application of an old device. This device-the 4-f or Van der Lugt correlator-has historically been used as a fast pattern classifier. Usually the correlation operation is employed as a matched filter, so that a maximum correlation peak corresponds to a well-matched pattern. The device described also uses the large peaks, but takes specific advantage of the fact that a zero-shift correlation is mathematically equivalent to a two-dimensional inner product. The authors describe a promising method for emulating …