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Phase-functioned neural networks for character control
2017
ACM Transactions on Graphics
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We present a real-time character control mechanism using a novel neural network architecture called a Phase-Functioned Neural Network. ...
In this network structure, the weights are computed via a cyclic function which uses the phase as an input. ...
PHASE-FUNCTIONED NEURAL NETWORK In this section we discuss the construction and training of the Phase-Functioned Neural Network (PFNN). ...
doi:10.1145/3072959.3073663
fatcat:jsmhirwlwfc2lfrjwx5vd2unba
Implementation of Dynamic Artificial Intelligence in Game Development
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The most important part of the NN character is the Neural Network Controller which will consist of the Neural network and the other controller functions such as linking the controller with the character ...
THE NEURAL NETWORK CHARACTER The Neural network character will be similar to the playable character in terms of appearance but with the differance the the character is controlled by a neural network controller ...
doi:10.35940/ijitee.k1217.09811s19
fatcat:2avawo4z6ffsfcldgzscbatpfy
Neural state machine for character-scene interactions
2019
ACM Transactions on Graphics
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We propose Neural State Machine, a novel data-driven framework to guide characters to achieve goal-driven actions with precise scene interactions. ...
To increase the precision to reach the goal during runtime, we introduce a control scheme that combines egocentric inference and goal-centric inference. ...
is to let the neural network learn distinct cyclic/non-cyclic phase functions suitable for different actions and goals, instead of using a single, fixed cyclic function as in PFNN [Holden et al. 2017 ...
doi:10.1145/3355089.3356505
fatcat:njyujaqlgvhlteg4gjoufeid6a
Adaptive B-spline network control for three-phase PWM AC-DC voltage source converter
1999
Proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems. PEDS'99 (Cat. No.99TH8475)
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A neural network control methodadaptive Bspline neural network for three-phase AC-DC voltage source converters that realizes a sinusoidal ac input current and unity power factor is discussed in this paper ...
Comparing to the other PWM techniques, the main advantage of the neural network is that it has excellent merit for nonlinear control and is adaptive enough to fit the environment change. ...
ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support of the Hong Kong Polytechnic University for this project. ...
doi:10.1109/peds.1999.794608
fatcat:hwvb6xrgtvdgrmy66aff3wtuzi
License Plate Recognition System using Back Propagation Neural Network
2014
International Journal of Computer Applications
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In this paper, Haar wavelet and back propagation neural network are used for license plate detection and feature extraction of license plate characters. ...
In recent years, template matching was used for license plate recognition but it is sensitive to noise. So, neural networks are used for recognition. ...
Back Propagation Neural Network Neural network is a mathematical model that is inspired by the structure and functional aspects of biological neural networks. ...
doi:10.5120/17395-7945
fatcat:yllptjyo6ncqfmlrv3ccvmzzim
Presenting a Deep Motion Blending Approach for Simulating Natural Reach Motions
[article]
2018
Eurographics State of the Art Reports
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In this paper, we propose a novel deep blending approach to simulate non-cyclical natural reach motions based on an extension of phase functioned deep neural networks. ...
With ongoing progress in artificial intelligence and neural networks, recent works present deep learning based approaches for motion synthesis, which offer great potential for modeling natural motions, ...
Phase Functioned Neural Network The proposed approach extends the concept of phase functioned neural networks, being first introduced by Holden et al. [HKS17] . ...
doi:10.2312/egp.20181010
fatcat:qtgcc6iln5hx7adrmjtt5xxwaq
Usage of Real Time Machine Vision in Rolling Mill
2021
Sustainability
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The method of optical character recognition using artificial neural networks is the basic algorithm of the system of automatic identification of billets and eliminates ambiguities during their further ...
the control system. ...
The neural network works in two basic phases [7] :
• Learning phase: (a) adjusting weights according to input patterns; (b) repeating the learning process. ...
doi:10.3390/su13073851
fatcat:5efjyrk6qzde5pws4sq4of6g5q
GENETIC ALGORITHM AND NEURAL NETWORK FOR OPTICAL CHARACTER RECOGNITION
2013
Journal of Computer Science
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The method mostly used for character recognition is the backpropagation network. ...
The accuracy in recognizing character differ by 10, 77%, with a success rate of 90, 77% for the optimized backpropagation and 80% accuracy for the standard backpropagation network. ...
This paper presents a new approach in optimizing backpropagation neural network training for optical character recognition. ...
doi:10.3844/jcssp.2013.1435.1442
fatcat:do4raryerfcjrgx3vhvw2lxway
Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)
2012
International Journal of Computer Applications
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Character data is used for training the neural network and SVM. The trained network is used for classification and recognition. ...
For the neural network, each character is resized into 70x50 pixels, which is directly subjected to training. ...
Of the several Kernel function used in SVM for classifying the characters, the linear kernel function yields the highest recognition accuracy of 94.8 and this accuracy is more than the neural network model ...
doi:10.5120/8466-2387
fatcat:wq4lkqa6fvbuth6tounkqldnhu
Evolving artificial neural networks that develop in time
[chapter]
1995
Lecture Notes in Computer Science
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which dictate early maturation of functional neural structure but not of nonfunctional structure. ...
which dictate early maturation of functional neural structure but not of nonfunctional structure. ...
(Functional units are units that are part of the functional network while nonfunctional units are part of the nonfunctional neural structure.) ...
doi:10.1007/3-540-59496-5_311
fatcat:7ft75iw47nak7j3c6j467mzvum
Biomimetic Underwater Vehicle Modeling Based on Neural Network
2011
IFAC Proceedings Volumes
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This paper introduces the basic control methods of a Biomimetic Underwater Vehicle, and a neural network model is designed for the vehicle. ...
Based on analysis of the experimental thrust data, linear function is used as activation function of the neurons. Finally, experimental data are used for neural network training and validation. ...
Based on thrust experimental results, linear function is used as activation function for the neural network layers. ...
doi:10.3182/20110828-6-it-1002.00382
fatcat:nnhtpg2sv5gkbiod6n4vkrnvxq
Page 695 of Mathematical Reviews Vol. , Issue 99a
[page]
1991
Mathematical Reviews
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Using the Lyapunov function method, they study the properties of stability and convergence for the neural network system. ...
The book begins with a review of electrical circuits, focusing on voltage-controlled oscillators (VCOs), where the time derivative of the output phase is a linear function of the input voltage. ...
Controlling Chaos in a Neural Network Based on the Phase Space Constraint
2003
International Journal of Modern Physics B
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In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. ...
The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the ...
Compared to the Hopfield neural network, the chaotic neural network possesses the characters of larger memory content and good tolerance, etc. ...
doi:10.1142/s0217979203022192
fatcat:nzwjqvbksjcbzd6agzxhpps35u
License Plate Character Recognition System using Neural Network
2011
International Journal of Computer Applications
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This paper uses two Neural Network techniques for character recognition, one is Back Propagation Neural Network and other one is Learning Vector Quantization Neural Network. ...
FEATURE EXTRACTION In this paper, as Neural Network is used for character recognition, Feature Extraction is an important step for training and simulating the Neural Network. ...
doi:10.5120/3147-4345
fatcat:66okb6sblrhejpqo7zepwnyjvy
Complex networks of simple neurons for bipedal locomotion
2009
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
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The resulting networks are then examined to discover neural structures that arise unusually often, lending some insight into the workings of otherwise opaque controllers. 978-1-4244-3804-4/09/$25.00 ©2009 ...
Our approach exchanges complexity of the neuron model for complexity of the network, gradually building a network of simple neurons capable of complex behaviors. ...
CONCLUSION This work presents evolved neural network controllers that show smooth motion, without the stiffness and phase artifacts generally associated with methods based on a fixed set of phases or states ...
doi:10.1109/iros.2009.5354077
dblp:conf/iros/AllenF09
fatcat:fs4yjvbfzjh4vphxpqafrhrvye
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