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Towards using neural networks to perform object-oriented function approximation
2008
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
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We present the implementation of two translation algorithms that aid in performing object-oriented function approximation. ...
The algorithms presented in this paper represent a novel approach to neural-symbolic integration that allows for symbolic data in the form of objects to be translated to a scalar representation that can ...
CONCLUSIONS Our results from sorting have shown that the translation algorithms presented can be used as a step towards Object Oriented function approximation in principle. ...
doi:10.1109/ijcnn.2008.4634271
dblp:conf/ijcnn/TaylorBK08
fatcat:qztcxqbe4bhgterijwsvhxshpe
Modeling Orienting Behavior and Its Disorders with "Ecological" Neural Networks
2007
Journal of Cognitive Neuroscience
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The results showed that: (1) Despite being able to see the entire visual scene without moving their eye, agents learned to orient their eye toward a peripherally presented object. (2) Neural networks whose ...
Here we describe a series of simulations involving neural networks which learned to perform their task by self-organizing their internal connections. ...
Moreover, using a simulation setup similar to that used in the present work, Calabretta et al. (2004) showed that neural networks which develop orienting behavior are better able to generalize to new ...
doi:10.1162/jocn.2007.19.6.1033
pmid:17536973
pmcid:PMC2231571
fatcat:z5ry4zxtpbb7nchacsr5guon4a
Orientation detection of fruits by means of convolutional neural networks and laser line projection for the automation of fruit packing systems
2020
Procedia CIRP
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Functional subassemblies are identified, and a functional analysis is performed. ...
In our paper, we propose a method to determine the orientation of fruits by applying a specially trained Convolutional Neural Network to 2.5-dimensional image data generated by a combination of a color ...
The network was able to improve the orientation estimate of symmetric objects by means of a modified loss function. ...
doi:10.1016/j.procir.2020.05.092
fatcat:whdrtbd32rftdc2sprciowad7m
Inverse Kinematics Solution for Robot Manipulator based on Neural Network under Joint Subspace
2014
International Journal of Computers Communications & Control
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layer feedforward neural networks (SLFNs). ...
Neural networks with their inherent learning ability have been widely applied to solve the robot manipulator inverse kinematics problems. ...
Other schemes used neural networks to learn a mapping function from the world space to joint space. ...
doi:10.15837/ijccc.2012.3.1387
fatcat:klre7ctdqrcqbauyvsspkdlp4i
Towards Dense Object Tracking in a 2D Honeybee Hive
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. ...
Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently ...
We are grateful to Yoann Portugal for assistance with colony maintenance and image acquisition, as well as to Quoc-Viet Ha for work on the video acquisition and storage pipeline. ...
doi:10.1109/cvpr.2018.00440
dblp:conf/cvpr/BozekHMS18
fatcat:6abh3wf4evbetj3odhs4kljfou
Machine-learning-based estimation of reverberation time using room geometry for room effect rendering
2019
Proceedings of the ICA congress
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The method achieves a prediction accuracy of approximately 90% for most frequency bands. ...
This work presents a machine-learning-based method to estimate the reverberation time of a virtual room for auralization purposes. ...
ACKNOWLEDGEMENTS The project has received funding from from the Academy of Finland project no 317341, and from Nordic Sound and Music Computing Network (NordicSMC), project no.86892. ...
doi:10.18154/rwth-conv-239303
fatcat:ivixl5mwofdsnntfk3qoiup6ke
Towards dense object tracking in a 2D honeybee hive
[article]
2017
arXiv
pre-print
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We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. ...
Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently ...
We are grateful to Yoann Portugal for assistance with colony maintenance and image acquisition, as well as to Quoc-Viet Ha for work on the video acquisition and storage pipeline. ...
arXiv:1712.08324v1
fatcat:l7r7xy56cbgfnpv7b3smxybokm
A Software Framework for Optimization of Process Parameters in Material Production
2011
Applied Mechanics and Materials
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The general framework is outlined, which has been supplemented by a neural networks module in order to enable real time decision support. ...
Simulator based on meshless method with radial basis functions (RBF) has been utilized. ...
Sampled data has been used to train a two layer artificial neural network with sigmoid activation function. ...
doi:10.4028/www.scientific.net/amm.101-102.838
fatcat:bvwkyja2lnbxtlaozrkh5gfvbi
Product Cost Management Structures: a review and neural network modelling
2003
Australasian Journal of Information Systems
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Simulated data is used in neural network applications across activities that consume resources and deliver products, to generate information for monitoring and control decisions. ...
techniques of neural networks. ...
For example, an OO structure of the Counterpropagation Neural Network (CPN, Figure 3) indicates how a neural network system can be looked at under the object oriented system. ...
doi:10.3127/ajis.v11i1.140
fatcat:qllo27t75ngktlh3umywy47oai
Comparison of extremum seeking control algorithms for robotic applications
2012
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
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When noise is present, the neural network based optimizers are a better choice thanks to their hysteresis functions. ...
These techniques are categorized into five main groups: Sliding mode ESC, neural network ESC, approximation based ESC, perturbation based ESC and adaptive ESC. ...
Multivariate Extension: For the multivariate case, multivariate approximation techniques can be used to approximate the objective function locally. ...
doi:10.1109/iros.2012.6386180
dblp:conf/iros/CalliCJW12
fatcat:cnnvhmse25gtrixsvc52nvngqe
The Bayesian brain: the role of uncertainty in neural coding and computation
2004
Trends in Neurosciences
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[47] have designed a network architecture that uses gain-encoding to perform optimal Bayesian inferences. ...
A basis function network for optimal cue integration between a visual and an auditory input. The network is tuned to perform two tasks simultaneously. ...
doi:10.1016/j.tins.2004.10.007
pmid:15541511
fatcat:cv5sx6jtibfhxgzavstpmxbrcq
Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments
2014
International Journal of Computer and Electrical Engineering
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The computational burden on microcontrollers is reduced by using piecewise linearly approximated version of tangent-sigmoid activation function of neurons. ...
Index Terms-Navigation in complex environments, neural network, hurdle avoidance behavior, goal reaching behavior, real time implementation. ...
The other solution is to use RAM based neural networks that do not require any activation function. ...
doi:10.7763/ijcee.2014.v6.799
fatcat:ntllqrrggbh2bpfk2gzuwhw4hu
Fuzzy membership function based neural networks with applications to the visual servoing of robot manipulators
1994
IEEE transactions on fuzzy systems
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nonlinear mapping, where the structure of the FMF network is similar to that of radial basis function neural network which is known to be very effective in the function approximation. ...
Instead of analytically deriving the closed form of this mapping, a fuzzy membership function (FMF) based neural network incorporating a fuzzy-neural interpolating network is proposed to approximate the ...
To demonstrate the capability of the function approximation of our FMF network incorporating the proposed fuzzy-neural interpolating network, a simulation is performed with a function known as the Mexican ...
doi:10.1109/91.298449
fatcat:ushvzc2v4na55cvreothayk53i
Adding reinforcement learning features to the neural-gas method
2000
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
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We propose a new neural approach for approximating function using a reinforcement-type learning: each time the network generates an output, the environment responds with the scalar distance between the ...
Thus, this distance is the only information the network can use to modify the estimation of the multi-dimensional output. ...
When the learning procedure is completed, the neural network is ready to be used as a function approximator and adaptation is no longer performed. ...
doi:10.1109/ijcnn.2000.860827
dblp:conf/ijcnn/WinterMS00a
fatcat:5hpq7yhpprf4lf64gjykbiamge
Human Body Orientation Estimation using Convolutional Neural Network
[article]
2016
arXiv
pre-print
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For a more robust and accurate approach, we propose the light weight convolutional neural networks, an end to end system, for estimating human body orientation. ...
Previous studies used various components such as feature extractors and classification models to classify the orientation which resulted in low performance. ...
ACKNOWLEDGMENT Thanks to Christina Baek for proofreading and revision. ...
arXiv:1609.01984v1
fatcat:yqmgrb3ssbbvlm4wwcqqcgphni
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