Modeling The Tower Of Hanoi Using Neural Network

1993 ◽  
pp. 47-56
Author(s):  
Mohamed Othman ◽  
Mohd. Hassan Selamat ◽  
Zaiton Muda ◽  
Lili Norliya Abdullah
Keyword(s):  
Neural Network ◽  
Knowledge Representation ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Neural System ◽  
Neural Systems ◽  
Tower Of Hanoi ◽  
Artificial Neural ◽  
The Given

This paper discusses the modeling of Tower of Hanoi using the concepts of neural network. The basis idea of backpropagation learning algorithm in Artificial Neural Systems is then described. While similar in some ways, Artificial Neural System learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connection in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable qf reproducing the desired function within the given network. Key words: Tower of Hanoi; Backpropagation Algorithm; Knowledge Representation;

scholarly journals ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR

2017 ◽  
Vol 12 ◽  
pp. 99
Author(s):  
Martin Ruzek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Human Operator ◽  
Biological Neural Network ◽  
Artificial Learning ◽  
Artificial Neural

This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network. The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system. The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition.

An improved percentage rate accuracy in predicting mortality in hepatitis-c using an artificial neural network

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Daniel Matthias ◽  
I.N. Davies ◽  
O. Olumide
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Object Oriented Design ◽  
Intelligent Technique ◽  
Prediction Of Mortality ◽  
Artificial Neural

Background accurate prediction of mortality in Hepatitis-C (Hep C) is essential for policy action and planning. While studies have used artificial intelligent technique (e.g., artificial neural network (ANN)), their appropriateness to predicting mortality in hepatitis-c has been debated. This study presents an improved percentage rate accuracy that is capable of predicting whether a patient suffering from Hepatitis-C Virus (HCV) is likely to survive or die. The constructive research method was adopted for this study, while an Object Oriented Design Approach was adopted for the systems structural design. The Artificial Neural Network system was implemented using java programming language with many program modules and four (4) design classes namely; the Driver class that runs the application program, the Neural Network class, the Neuron and the Layer classes. The network was trained using back propagation machine learning algorithm, a learning rate of 0.8 and a learning error of 0.05. While the weights used for the training were random numbers ranging from -1.0 to +1.0. The maximum number of training sessions was set to 10000 assuming the network does not converge to the leaning error of 0.05. The result of the network showed 85% accuracy in predicting cases of the patients with positive hepatitis C virus that may survive and also 50% accuracy in predicting cases of patients with positive Hepatitis-C Virus (HCV) that may likely to die given the provided data. Neural network is a powerful classification and prediction tools that can help in predicting the outcome of Hepatitis-C virus (HCV) infections. Recommending experiment on the network architecture with a view to either increase the hidden layers or increasing the number of units in the hidden layer. Also, more extensive testing and training should be carried out to achieve the desired result.

scholarly journals LUNG DISORDERS DETECTION BASED ON IRISES IMAGE USING COMPUTATIONAL INTELLIGENT ART

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Anna Triwijayanti K. ◽  
Hadi Suwastio ◽  
Rini Damayanti
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Digital Camera ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Artificial Neural

Iridology as a way of revealing human organs and tissues conditions is done by iridologist by taking the image of both irises of the patients. This can be done by using a digital camera and observe each iris on the LCD display or connect the camera to a computer or a television set and observe it through the display. Research on computerized iridology has been performed before by using artificial neural network of back propagation, which is a kind of supervised learning algorithm, as the classifier [13]. Such system should be able to retain its stability while still being plastic enough to adapt to arbitrarily input patterns. Adaptive Resonance Theory (ART), another kind of artificial neural network which uses unsupervised learning algorithm, has some important traits, such as real-time learning, self-stabilizing memory in response to arbitrarily many input patterns, and fast adaptive search for best match of input-to-stored patterns [9]. That way, ART architecture is expected to be the best stable and adaptable solution in changing environment of pattern recognition. In this research, the lung disorders detection is simply designed through the steps of segmentation, extraction of color variations, transformation of lung and pleura representation area in iris image to binary form as the input of ART 1, and pattern recognition by ART 1 neural network architecture. With 32 samples and 4 nodes of output layer of ART1, the system is able to determine the existences of the four stadiums of lung disorders (acute, subacute, chronic and degenerative) in relatively short time process (approximately 1.8 to 3.2 seconds) with the accuracy of stadium recognition 91.40625% by applying the vigilance parameter value of 0.4.Keywords: iridology, lung, pleura, segmentation, ART 1 neural network

scholarly journals Artificial Neural Network Based shafts surface pressures Analysis

2021 ◽  
Vol 2089 (1) ◽  
pp. 012046
Author(s):  
B V Ramana Murthy ◽  
Vuppu Padmakar ◽  
B N S M Chandrika ◽  
Satya Prasad Lanka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural System ◽  
Scientific Model ◽  
System Models ◽  
Learning Rules ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Forecast Quality

Abstract This paper exhibits a development of an Artificial Neural Network (ANN) as an instrument for investigation of various parameters of a framework. ANN comprises of various layers of straightforward handling components called as neurons. The neuron performs two capacities, to be specific, assortment of sources of info and age of a yield. Utilization of ANN gives diagram of the hypothesis, learning rules, and uses of the most significant neural system models, definitions and style of Computation. The scientific model of system illuminates the idea of sources of info, loads, adding capacity, actuation work and yields. At that point ANN chooses the sort of learning for modification of loads with change in parameters. At long last the examination of a framework is finished by ANN execution and ANN preparing and forecast quality.

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

scholarly journals A Survey on Techniques used in Medical Imaging Processing

2021 ◽  
Vol 2089 (1) ◽  
pp. 012013
Author(s):  
Priyadarshini Chatterjee ◽  
Dutta Sushama Rani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Medical Image Analysis ◽  
Artificial Neural ◽  
Automated Screening ◽  
To Come

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

Sign in / Sign up

Export Citation Format

Share Document