Improving Robotic System Robustness via a Generalised Formal Artificial Neural System

The advancement of industry apparatuses for some methods with specific tasks to control the working of a few actuators on the field. Among these actuators, Permanent magnet synchronous motor drives are a mainly all-inclusive machine. Proficient utilization of hesitance torque, generally effectiveness, minor misfortunes and smaller size of the motor are the principle attractions of PMSM when contrasted and different drivers. Precise and rapid torque reaction is one of the parameters to determine differentiating arrangements in the ongoing past. The field-situated power perceived the likely and vigorous answer to accomplish these prerequisites to empower the figuring of streams and voltages in different parts of the inverter and motor under transient and consistent conditions. The primary objective of this paper is to investigate Artificial Neural Network based control of speed for PMSM in both open and closed loop under no-load and loaded condition. A shut circle control framework with ANN procedure in the speed circle intended to work in steady torque and transition debilitating districts. MATLAB reproduction performed in the wake of preparing the neural system (directed learning), results for reference control applications are adequate and appropriate in the process business. Speed control in shut circle at different stacking conditions talked about in detail.

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Application of artificial neural system algorithms to image analysis of roots in soil, I. initial results

Geoderma ◽

10.1016/0016-7061(92)90057-e ◽

1992 ◽

Vol 53 (3-4) ◽

pp. 237-253 ◽

Cited By ~ 9

Author(s):

Edward A. Nater ◽

Keith D. Nater ◽

John M. Baker

Keyword(s):

Image Analysis ◽

Neural System ◽

Artificial Neural ◽

Initial Results

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Modeling of Stage-Discharge Using Back Propagation ANN, ANFIS, and WANN-based Computing Technique

10.21203/rs.3.rs-696059/v1 ◽

2021 ◽

Author(s):

Ravi Shukla ◽

Pravendra Kumar ◽

Dinesh Kumar Vishwakarma ◽

Rawshan Ali ◽

Rohitashw Kumar ◽

...

Keyword(s):

Water Resource Management ◽

Hydrological Modeling ◽

Fuzzy Inference ◽

Back Propagation ◽

Activation Function ◽

Neural System ◽

Back Propagation Algorithm ◽

Inference System ◽

Computing Technique ◽

Artificial Neural

Abstract The development of the stage-discharge relationship is a fundamental issue in hydrological modeling. Due to the complexity of the stage-discharge relationship, discharge prediction plays an essential role in planning and water resource management. The present study was conducted for modeling of discharge at the Gaula barrage site in Uttarakhand state of India. The study evaluated, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Wavelet-Based Artificial Neural System (WANN) based models to estimate the discharge. The daily data of 12 years (2007-2018) were used to train and test the models. The Gamma test was used to identify the best model for discharge prediction. The input data having a stage with one-day lag and discharge with one and two-days lag and current-day discharge as output was used for discharge modeling. In the case of ANN models, the back-propagation algorithm and hyperbolic tangent sigmoid activation function was used. WANN used Haar, a trous based wavelet function. In ANFIS models, triangular, psig, generalized bell, and Gaussian membership functions were used to train and test the models. The models were evaluated qualitatively and quantitatively using correlation coefficient, root means square error, Willmott index, and coefficient of efficiency. It was found that ANFIS model performed better than ANN and WANN-based models for discharge prediction at the Gaula barrage.

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Artificial Neural Network Based shafts surface pressures Analysis

Journal of Physics Conference Series ◽

10.1088/1742-6596/2089/1/012046 ◽

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.

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Dynamic control of an artificial neural system: the property inheritance network

Applied Optics ◽

10.1364/ao.26.004961 ◽

1987 ◽

Vol 26 (23) ◽

pp. 4961 ◽

Cited By ~ 5

Author(s):

Thomas W. Ryan ◽

C. L. Winter ◽

Charles J. Turner

Keyword(s):

Dynamic Control ◽

Neural System ◽

Artificial Neural ◽

Property Inheritance ◽

Inheritance Network

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Modeling The Tower Of Hanoi Using Neural Network

Jurnal Teknologi ◽

10.11113/jt.v21.1071 ◽

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;

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IMPROVEMENT IN THE DETERMINATION OF ELEMENTAL CONCENTRATIONS IN PIXE ANALYSES USING ARTIFICIAL NEURAL SYSTEM

International Journal of PIXE ◽

10.1142/s0129083508001491 ◽

2008 ◽

Vol 18 (03n04) ◽

pp. 147-155

Author(s):

R. CORREA ◽

M. I. DINATOR ◽

J. R. MORALES ◽

P. A. MIRANDA ◽

S. A. CANCINO ◽

...

Keyword(s):

Nuclear Physics ◽

Back Propagation ◽

Neural System ◽

Major Elements ◽

Back Propagation Algorithm ◽

Fast Analysis ◽

Error Back Propagation ◽

Elemental Concentrations ◽

Artificial Neural ◽

Time Required

An Artificial Neural System, ANS, has been designed to operate in the analysis of spectra obtained from a PIXE (Proton Induced X-ray Emissions) application. The special designed ANS was used in the calculation of the concentrations of the major elements in the samples. Neural systems using several feed-forward ANN of similar topology working in parallel were trained with error back propagation algorithm using sets of spectra of known elemental concentrations. Following the training phase of the neural networks, other PIXE spectra were analyzed with this methodology providing unknown elemental concentrations. ANS results were compared with results obtained by traditional computer codes like AXIL and GUPIX, obtaining correlations factors close to one. The rather short time required to process each spectrum, of the order of microseconds, allows fast analysis of a large number of samples. Here we present applications of ANS in the PIXE analyses of samples of organic nature like liver, gills and muscle from fishes. ANS results were compared with elemental concentrations obtained in a previous application where a single ANN was used for each analyzed element. PIXE analyses were performed at the Nuclear Physics Laboratory of the University of Chile, using 2.2 MeV proton beams provided by a Van de Graaff accelerator.

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